# Excitonic Absorption Signatures of Twisted Bilayer WSe2 by Electron Energy-
Loss Spectroscopy

Steffi Y. Woo<EMAIL_ADDRESS>Laboratoire de Physique
des Solides, Université Paris-Saclay, CNRS UMR 8502, F-91405, Orsay, France
Alberto Zobelli  Laboratoire de Physique des Solides, Université Paris-Saclay,
CNRS UMR 8502, F-91405, Orsay, France  Robert Schneider  Institute of Physics
and Center for Nanotechnology, University of Münster, Münster, Germany  Ashish
Arora  Institute of Physics and Center for Nanotechnology, University of
Münster, Münster, Germany  Indian Institute of Science Education and Research,
Dr. Homi Bhabha Road, 411008 Pune, India  Johann A. Preuß  Institute of
Physics and Center for Nanotechnology, University of Münster, Münster, Germany
Benjamin J. Carey  Institute of Physics and Center for Nanotechnology,
University of Münster, Münster, Germany  Steffen Michaelis de Vasconcellos
Institute of Physics and Center for Nanotechnology, University of Münster,
Münster, Germany  Maurizia Palummo  Dipartimento di Fisica, and European
Theoretical Spectroscopy Facility (ETSF), Università di Roma Tor Vergata, Via
della Ricerca Scientifica 1, 00133 Rome, Italy  Rudolf Bratschitsch  Institute
of Physics and Center for Nanotechnology, University of Münster, Münster,
Germany  Luiz H. G. Tizei<EMAIL_ADDRESS>Laboratoire de Physique des Solides, Université Paris-Saclay, CNRS UMR 8502,
F-91405, Orsay, France

###### Abstract

Moiré twist angle underpins the interlayer interaction of excitons in twisted
van der Waals hetero- and homo-structures. The influence of twist angle on the
excitonic absorption of twisted bilayer tungsten diselenide (WSe2) has been
investigated using electron energy-loss spectroscopy. Atomic-resolution
imaging by scanning transmission electron microscopy was used to determine key
structural parameters, including the nanoscale measurement of the relative
twist angle and stacking order. Detailed spectral analysis revealed a
pronounced blueshift in the high-energy excitonic peak C with increasing twist
angle, up to 200 meV when compared to the AA′ stacking. The experimental
findings have been discussed relative to first-principle calculations of the
dielectric response of the AA′ stacked bilayer WSe2 as compared to monolayer
WSe2 by employing the GW plus Bethe-Salpeter equation (BSE) approaches,
resolving the origin of higher energy spectral features from ensembles of
excitonic transitions, and thus any discrepancies between previous
calculations. Furthermore, the electronic structure of moiré supercells
spanning twist angles of $\sim$9.5–46.5∘ calculated by density functional
theory (DFT) were unfolded, showing an uplifting of the conduction band
minimum near the $Q$ point and minimal change in the upper valence band
concurrently. The combined experiment/theory investigation provides valuable
insight into the physical origins of high-energy absorption resonances in
twisted bilayers, which enables to track the evolution of interlayer coupling
from tuning of the exciton C transitions by absorption spectroscopy.

##  I Introduction

Semiconducting transition metal dichalcogenides (TMDCs), such as tungsten
diselenide (WSe2), belong to a family of two-dimensional (2D) materials that
exhibit fascinating electronic and optical properties. Due to interlayer
coupling, bulk and multi-layered molybdenum- and tungsten-based TMDCs are
indirect gap semiconductors, while their monolayers exhibit a crossover to a
direct band gap. The strong spin-orbit interactions in TMDCs lead to spin-
splitting of few hundred meV in the valence band and few to tens of meV in the
conduction band, making TMDCs of interest for potential valleytronics
applications.Wang  _et al._ (2018a) The promising versatility of engineering
with TMDCs is rooted in the flexibility of being artificially fabricated into
van der Waals homo- or heterostructures. With the recent interest in magic-
angle twisted bilayer graphene for the appearance of flat bands Cao  _et al._
(2018), moiré superlattices of TMDCs naturally become a viable candidate for
seeking similar phenomena Jin  _et al._ (2019). To date, in twisted
homobilayer WSe2 alone, evidence of low-energy flat bands Zhang  _et al._
(2020) that could support emergent electronic phases for a continuum of low
twist angles, such as superconductivity An  _et al._ (2020) and correlated
insulator states Wang  _et al._ (2020) have been reported. Modification of the
layer stacking as a function of twist alters the local atomic registry. As
such, the interlayer coupling strength in homostructures has demonstrated to
also be sensitive to twist angle in the case of bilayer MoS2 Castellanos-Gomez
_et al._ (2014) and WS2 Yan  _et al._ (2019). Recent reports on domain
formation caused by atomic reconstruction as a result of interlayer
interactions of TMDC moiré superlattices with very low (near 0∘) twist angles
in bilayer MoSe2Sung  _et al._ (2020), WS2 and WSe2Weston  _et al._ (2020) and
double bilayer WSe2An  _et al._ (2020) have opened yet another avenue towards
novel electronic and excitonic properties.

A major obstacle in the field involves correlating the optical response with
imaging of the local structure of a nanometric moiré lattice, where efforts
have turned towards various electron microscopies and scanning probe
microscopies Andersen  _et al._ (2021); McGilly  _et al._ (2020); Weston  _et
al._ (2020). The latest generation of electron monochromators with energy
resolution of 10 meV and below has enabled the measurement of phonons in
hexagonal boron nitride (h-BN) and excitonic absorption in various monolayer
TMDCs Tizei  _et al._ (2015) in a transmission electron microscope with
subwavelength spatial resolution. Aside from offering an exceptional
combination of spatial and energy resolution, the high-energy incident
electrons in electron energy-loss spectroscopy (EELS) have facile access to
high-energy excitations of a few eV and beyond, unlike limitations met by
optical techniques.

In this work, the combined high spatial and spectral resolution of aberration-
corrected scanning transmission electron microscopy (STEM) and monochromated
EELS in the low-loss regime were used to investigate the optical excitation
response of atomically-thin WSe2, specifically in twisted bilayer WSe2
covering a large range of moiré angles. Relevant characteristics of the local
atomic structure were also obtained within the same platform, including the
twist angle and layer stacking. Furthermore, first-principle calculations of
the electronic structure modifications in twisted bilayers and expected
optical response relative to monolayers and zero-twist bilayers were used to
interpret the changes in high-energy spectral features measured in EELS.

##  II Results

Atomically-thin WSe2 flakes have been mechanically exfoliated from a bulk
synthetically-grown crystal Tonndorf  _et al._ (2013), and transferred onto a
carbon-coated Si3N4 TEM grid with periodic 1 $\mu$m-sized holes. An optical
microscopy reflectance image of exfoliated WSe2 transferred onto the holey
Si3N4 TEM grids with regions of different layer thickness is presented in Fig.
1(b), including the monolayer (ML) area marked by a dashed line and the
adjacent trilayer (TL) regions. Atomically-resolved imaging has been performed
on an aberration-corrected Nion UltraSTEM200 operated at 60 keV and
monochromated EELS was performed on a modified Nion HERMES-S200 (also known as
ChromaTEM) operated at 60 keV with the sample cooled to cryogenic temperatures
(T $\approx$ 150 K) as depicted in the schematic shown in Fig. 1(a). High-
angle annular dark-field (HAADF) imaging encompasses Rutherford scattering
towards high angular ranges whose cross-section approximates proportionally to
the atomic-number (Z) by $\sim$Z1.7. STEM-HAADF imaging of such freestanding
WSe2 monolayers, shown in the inset of Fig. 1(d), demonstrates the
distinguishable intensity difference between the W and Se2 atomic columns.

The EELS loss function, Im${\\{-1/\epsilon\\}}$, of layered materials like
TMDCs depends on the in-plane component of its complex dielectric function
$\epsilon=\epsilon_{1}+i\epsilon_{2}$ Tizei  _et al._ (2015). As thickness
approaches atomically-thin towards monolayers, the loss function becomes
dominated by surface effects, and therefore it approximates to
Im${\\{\epsilon\\}}$. It is thus equivalent to measuring the 2D material’s
absorption function ($\alpha$), and can reflect the optical excitation
response, including the optical bandgap, and interband transitions. The
excitonic absorption signatures of both freestanding and h-BN encapsulated
WSe2 monolayers from low-loss EELS with the elastic (zero-loss) peak
background subtracted demonstrates a good general correspondence to the
optical absorption spectrum in Fig. 1(d)Arora  _et al._ (2015); Schmidt  _et
al._ (2016). With the exception of the additional broadening in the
freestanding WSe2 Shao  _et al._ (2022), the four dominant peaks, labeled as
A, B, C, and D, are all well-reproduced in shape and energy positions. The
theoretical understanding of the physical origin of each of these resonances
in WSe2 monolayer, including the high-energy spectral features, will be
discussed in Section III.

Figure 1: (a) Schematic of the electron microscope set-up including an
electron monochromator, liquid nitrogen cooling sample holder, and a light
collection system (not used in this work). (b) Optical microscopy image of a
mechanically-exfoliated WSe2 flake transferred onto a holey Si3N4 TEM grid.
(c) Schematic illustrating how nanometric fragments of (twisted) bilayers and
trilayers were likely formed during the exfoliation and transfer process. (d)
Comparison of low-loss EELS spectra measured from freestanding (black) and
h-BN encapsulated (red) WSe2 monolayer, and optical absorption spectrum of
h-BN encapsulated WSe2 monolayer (blue), inset with a STEM-HAADF images of
WSe2 monolayer illustrating distinct contrast between the W and Se2 atomic
columns.  Figure 2: (a,b) STEM-HAADF images of WSe2 monolayer (ML) folded
along a zig-zag direction direction indicated in the inset image fast-Fourier
transform in (a), resulting in 0∘ bilayers with AA′ (2H) stacking order. (c)
Atomic model for AA′ stacked WSe2 bilayer with armchair (ac) and zig-zag (zz)
directions noted in the projected view, and (d) multislice STEM-HAADF image
simulation corresponding to the atomic model. (e) Intensity line profiles
comparing experimental and simulated images along the selected areas marked by
red and blue square brackets in (b) and (d), respectively.  Figure 3: STEM-
HAADF images of WSe2 bilayers with (a) 13∘, (b) 5.5∘, and (e) 3.4∘ relative
twist angle as measured from the image FFT of the larger twist angles in (c),
(d), respectively. The moiré unit cells are highlighted by the dotted lines in
(a), (b), and (e). In the image FFT, the red marks the orientation of the
underlying monolayer, and the blue marks the orientation of the additional
layer. (f) Atomic structure model of a moiré superlattice of twisted bilayer
WSe2 of 3.9∘ twist angle and R-type stacking, with the moiré unit cell
outlined in black, the various high-symmetry stacking identified by colored
circles, and bridge site (Br) labelled. (g) Monochromated EELS spectra from
twisted bilayer WSe2 with various moiré angles compared to a representative
monolayer (ML) offset in energy relative to the A exciton. The dotted lines
are a guide to show the invariance and changes in the different exciton energy
positions.

In addition to freestanding WSe2 monolayers, sub-micron fragments of bilayers
(BLs) and trilayers (TLs) with variable relative twist angle between 0–30∘
were also common occurrences due to folding during the mechanical exfoliation
and transfer process [cf. Fig. 1(c) and Fig. S3]. The nature by which bilayers
were formed can help shed light on their stacking order. In the case of those
formed by folding of free edges, folding of monolayers along a zig-zag
direction results in a bilayer with an aligned configuration of 0∘ relative
twist angle and AA′ stacking order (following the nomenclature proposed in
ref. He  _et al._ , 2014), as shown in Fig. 2(a–c). AA′ (2H) bilayer stacking
corresponds to the most energetically favorable configuration He  _et al._
(2014), typical of bilayers obtained by mechanical exfoliation from bulk
crystals Sarkar  _et al._ (2019). STEM-HAADF image intensity was used to
deduce the stacking in the bilayers by comparison with line profiles from
multislice image simulations [Fig. 2(d,e)]. Fig. S3(e) illustrates
schematically how folding of monolayers along a zig-zag direction (top), or in
between zig-zag and armchair directions (bottom) can result in bilayers of
zero and non-zero twist angles, respectively. The high twist angles of the 13∘
and 28∘ bilayers summarized in Fig. S3 correspond to commensurate moiré angles
with some of the smallest coincidence site lattices in homobilayers Zhao  _et
al._ (2022), and falls under the second category when assessing the
crystallography of the fold. The second-order reflection (armchair direction)
circled in red in Fig. S3(d) indicates that for twist angle of 13∘, the
folding normal (marked by the green arrow) lies closer towards an armchair
direction, such that this would result in R-type (or AA) stacking in the
bilayer. Other notable features include the seldom Se-vacancy in monolayers
Zheng  _et al._ (2019), and bands of oxide product made up of atomic clusters
of tungsten from preferential oxidation at flake edges [cf. Fig. 2(b), Fig.
S3(c,g)].

The non-zero relative twist angles routinely observed vary from high twist
angles [Fig. 3(a) and Fig. S3] that generate sub-nanometer moiré periodicities
to $\sim$10 nm periods for few degree twists [Fig. 3(b,e)]. The relative twist
angles were measured from the image fast Fourier-transform (FFT), including
examples shown in Fig. 3(c,d), and confirmed using nano-beam electron
diffraction, where the latter is less sensitive to effects of scan distortion
in STEM imaging. Well-defined hexagonal moiré patterns with few-nanometer
periodicity are also evident in the STEM-HAADF images for the low twist angles
in Fig. 3(b,e) and Fig. S2. The moiré superlattice (as highlighted by a dotted
line in the images) of the WSe2 homobilayers is composed of regions of high-
symmetry stacking, namely, AA, AB, and BA stacking in the case of R-type
stacking Gogoi  _et al._ (2019). As shown in the moiré superlattice structure
model in Fig. 3(f), it is the bridge sites (marked Br) connecting adjacent AB
and BA stacked regions, which also have their own unique local alignment, that
make up the hexagonal pattern outlines of the twisted bilayers observable in
the STEM-HAADF images. Unlike the common AA′ stacking, these aforementioned
R-type stacking configurations lack inversion symmetry, such that the K
($K^{\prime}$)-points of the joint Brillouin zone are inequivalent and the
spins of the upper and lower split bands in individual layers are instead
identical at a given valleySchneider  _et al._ (2019). For twisted bilayers,
the energy of the indirect $KQ$ transition has been shown to depend on twist-
angle and atomic registry in MoS2 van der Zande  _et al._ (2014), WS2 Zheng
_et al._ (2015), and more recently in WSe2 Wang  _et al._ (2018b); Scuri  _et
al._ (2020); Merkl  _et al._ (2020).

Low-angle annular dark-field (LAADF) imaging in STEM offers more diffraction
contrast sensitivity, and can be used to image the moiré superlattice over
long-range (hundreds of nm), thus particularly useful to illustrate
distortions in the local periodicity over long distances Weston  _et al._
(2020). At twist angles towards 5∘, STEM-LAADF imaging gives a periodic
pseudo-atom like contrast that corresponds to the various high-symmetry
stacking points in the moiré superlattice. Distortions in the moiré lattice at
ripples, cracks or towards edges of the twisted WSe2, such as shown in Fig.
S4(g,i), lead to release of strain and thus reconstruction into domains (an
expansion of specific high-symmetry points). The domain contrast is further
manifested at the lowest twist angle of 2.3∘, where arrays of triangular
domains with boundaries in dark contrast are arranged in a six-fold fashion
[as marked by alternating purple and turquoise triangles in Fig. S4(c)]
continuous over the entirety of the few hundred nm-sized twisted bilayer and
trilayer. The domain boundary geometry differs between R-type (AA) and H-type
(AA′) stacking in twisted homobilayer TMDCs Weston  _et al._ (2020), taking on
a triangular geometry and kagome-like pattern dominated by hexagonal regions,
respectively. Both geometries are governed by atomic reconstruction where some
of the high-symmetry stacking regions become more energetically favorable
towards low twist angles.

The purpose of the STEM-LAADF imaging on twisted WSe2 bilayers is effectively
two-fold: firstly to identify lattice distortions and the occurrence of atomic
reconstruction; and secondly, the domain boundary geometry was used to
identify the stacking order in lower twist angles. Three of the five twisted
bilayers formed by stacking presented in Fig. S4 have been identified to have
R-type (AA) stacking. In addition, comparative STEM-HAADF images of regions
with atomic reconstruction [see Fig. S4(d) and (h)] confirms the domain
boundary are the bridge sites (Br), and the triangular domains are made up of
AB/BA stacking configuration as outlined by the purple triangle in Fig. S4(d).
Observation of atomic reconstruction over long-range in only the 2.3∘ twisted
bilayer WSe2 validates the calculated crossover angle for R-type stacked
bilayer TMDCs of $\theta^{\circ}_{3R}$ $\sim$ 2.5∘ by Enaldiev et al.Enaldiev
_et al._ (2020), below which the bilayers transition from a rigid rotation to
a lattice-reconstructed regime.

The excitonic absorption signatures of twisted WSe2 bilayers from low-loss
EELS are presented in Fig. 3(g) aligned in energy with respect to the A
exciton alongside a spectrum from a WSe2 monolayer for comparison. The four
excitonic resonances are also prominently reproduced in the case of twisted
bilayers. The energy separation between A and B exciton listed in Table 1,
which is linked to the spin-orbit coupling, remains relatively constant with
the number of layers, as well as twist angle. Small shifts in the A exciton,
coupled with simultaneous rigid shifts of the B exciton are visible in the
unadjusted spectra in Fig. S1(c) and they can be attributed to local strain
Schmidt  _et al._ (2016) or unintentional doping. It is worth noting the small
peaks $\sim$300 meV below the A exciton [Fig. 3(g) or at 1.3–1.4 eV as
presented in Fig. S1(c)] is likely of the same origin as the so-called subgap
exciton peak measured using momentum (q)-resolved EELS at non-zero q in
various TMDCs including WSe2 Hong  _et al._ (2020).

Table 1: Fitting of the A, B, C, D excitonic peak energy positions (XA, XB, XC, XD) obtained from the EELS spectra in Fig. S1(c), and the relative energy difference between A and B exciton (A–B) governed by valence band splitting at $K$-point, B and C exciton (B–C), and A and C exciton (A–C). Twist angles marked by asterisks (*) are relative twist angles because its stacking order remains undetermined and can also be 60 – $\theta$. Twist angle (∘) |  XA (eV) |  XB (eV) |  XC (eV) |  XD (eV) |  $\Delta_{A-B}$ (eV) |  $\Delta_{B-C}$ (eV) |  $\Delta_{A-C}$ (eV)
---|---|---|---|---|---|---|---
Monolayer | $1.696\,65$ | $2.145\,49$ | $2.590\,33$ | $3.023\,47$ | 0.449 | 0.445 | 0.894
0 | $1.751\,22$ | $2.248\,06$ | $2.387\,63$ | $2.950\,03$ | 0.497 | 0.140 | 0.636
2.3 | $1.753\,06$ | $2.225\,15$ | $2.416\,46$ | $2.959\,70$ | 0.472 | 0.191 | 0.663
3.4 | $1.711\,44$ | $2.160\,72$ | $2.420\,01$ | $2.934\,20$ | 0.449 | 0.259 | 0.709
4.1* | $1.749\,08$ | $2.195\,68$ | $2.447\,03$ | $2.973\,94$ | 0.447 | 0.251 | 0.698
5.5 | $1.713\,09$ | $2.158\,18$ | $2.480\,09$ | $2.970\,57$ | 0.445 | 0.322 | 0.767
7.2* | $1.762\,81$ | $2.223\,85$ | $2.486\,13$ | $2.952\,03$ | 0.461 | 0.262 | 0.723
13 | $1.697\,22$ | $2.142\,72$ | $2.471\,60$ | $2.939\,10$ | 0.446 | 0.329 | 0.774
28 | $1.743\,17$ | $2.184\,94$ | $2.516\,73$ | $2.987\,75$ | 0.442 | 0.332 | 0.774

Comparing different twist angles in the bilayers also show sizable shifts in
the C exciton energy up to 200 meV, which subsequently alter drastically the
overall shape of the spectrum at the B–C transitions, with extremes between
the aligned (0∘ and 60∘) and anti-aligned (towards 30∘) cases suggesting
underlying differences in interlayer coupling. The exciton peak shifts are
quantitatively determined by peak fitting to the second-derivative treated
with Savitzky-Golay filtering using multiple Gaussians, four in total, each
corresponding to a structure in each EELS spectrum. The results of the peak
fitting are summarized in Table 1 and displayed graphically in Fig. S1(e).
Consistent with optical absorption Arora  _et al._ (2015); Zhao  _et al._
(2013), the layer thickness dependence of A, B, and C exciton resonances of
few-layered WSe2 measured using monochromated EELS shows a pronounced decrease
in the C and D exciton energies between the monolayer and 0∘ bilayer with AA′
stacking in Fig. 3(c).

Furthermore, the C exciton energy continues to show the same decreasing trend
with the number of layers when comparing bilayers and trilayers of the same
twist angle [Fig. S2(b)]. The more pronounced shifts of the C and D excitonic
peaks relative to the A and B excitons with layer thickness suggests an
association to the localization of the electronic states involved in the
respective transitions, in particular the orbital character of the chalcogen
atoms (Se $p$-orbitals in this case) that contribute most to the interlayer
coupling Zhang  _et al._ (2015). Specifically, the valence band maximum (VBM)
at $K$-point exhibits in-plane $p_{x,y}$ character, while the VBM and
conduction band minimum (CBM) towards $\Gamma$-point displays mainly out-of-
plane $p_{z}$ character Voß  _et al._ (1999) and thus most strongly affected
by interlayer separation in few-layered WSe2; the $p$-orbital contribution
shows a mixture of $p_{x,y}$ and $p_{y}$ character at $Q$-point [see the
orbital-projected band structure for AA′ WSe2 in Fig. S5]. This corroborates
well with the expected changes in the band structure between monolayer towards
bulk WSe2, namely the appearance of an indirect gap $K$-$Q$ transition due to
the downshift of the $Q$-valley overtaking the $K$-point as CBM beyond a
monolayer. For the twisted bilayer WSe2 with increasing relative twist angle,
the C exciton energy blueshifts by $\sim$200 meV [cf. Table 1 and Fig. S1(e)],
indicative of an upshift in the CBM at $Q$-valley. Recent studies on twisted
bilayer WSe2 reported similar blueshifting in the indirect $KQ$ interlayer
exciton emission energy as a function of twist angle, reaching a maximum at
$\theta$ = 30∘, while relatively minimal change in the direct $KK$ intralayer
exciton (XA) in comparison Scuri  _et al._ (2020); Merkl  _et al._ (2020).
Therefore the phonon-assisted indirect $KQ$ exciton energy directly reflects
the interlayer electronic coupling strength, which is strongest at 0∘ and 60∘
Liu  _et al._ (2014). Raman spectroscopy is typically used as an indicator of
the mechanical (i.e. vibrational) interlayer coupling Castellanos-Gomez  _et
al._ (2014); Liu  _et al._ (2014); Sarkar  _et al._ (2019). In a similar
manner, energy shifts of the C exciton energy can also gauge the electronic
interlayer coupling effects from absorption-based techniques such as EELS,
indicating a reduction in its strength with moiré angle towards 30∘ in twisted
WSe2 bilayers.

##  III Discussion

Fundamental insights on the excitonic response of TMDCs can be successfully
acquired using the GW+BSE (Bethe-Salpeter equation) method on top of DFT
calculations Komsa and Krasheninnikov (2012); Qiu  _et al._ (2016); Marsili
_et al._ (2021). In the case of W-based TMDC monolayers, the lowest energy
exciton A is mainly composed of transitions near the $K$ point from the VBM to
the second unoccupied state in the conduction band (CBM+1) which has the same
spin-character Hong  _et al._ (2021); Gillen (2021); Marsili  _et al._ (2021).
The B peak has a more complex character but it is mostly formed by transitions
near $K$ from VBM$-$1 to CBM. As discussed in the literature Hong  _et al._
(2021); Gillen (2021), higher energy spectral features cannot be linked to
individual excitons but arise from an ensemble of excitonic transitions very
close in energy. The complex TMDCs excitonic spectra is therefore usually
broadened to reproduce the same number of peaks seen in experiments. However
these structures may not present a homogeneous excitonic character and result
from a superposition of excitons belonging to different orders of distinct
Rydberg series. This situation has led to an uneven nomenclature Zhao  _et
al._ (2013); Kozawa  _et al._ (2014); Gillen and Maultzsch (2017); Hong  _et
al._ (2021); Gillen (2021), and particular attention should be paid when
comparing different references.

The character of the excitonic transitions in the AA′ bilayer cannot be
deduced a priori from the spectroscopic response of the monolayer. Therefore,
the AA′ bilayer is treated explicitly, along with the monolayer case, via a
non-collinear GW+BSE approach (computational details can be found in the SI).
This method has been shown to provide good agreement with experiments for the
energy separation of the A and B excitons compared to a perturbative treatment
of spin-orbit coupling Marsili  _et al._ (2021). The imaginary part of the
dielectric function $\epsilon_{2}$ for the monolayer and AA′ bilayer WSe2
together with the oscillator strengths of the main excitonic transitions are
shown in Fig. 4(a) and (c). For the sake of comparison with experiments, four
energy windows centered at local maxima of $\epsilon_{2}$ have been defined
for the mono- and bilayer spectra, respectively, which can be linked to the
experimental peaks A–D [shaded regions in Fig. 4(a,b)]. Fig. 4(c,d) presents
the weight in the reciprocal space of the transitions contributing to each of
these peaks: for each exciton $\lambda$ in a given energy window, the weights
$\sum_{vc}A^{cvk}_{\lambda}$ of the electron-hole pairs of wave vector $k$ are
considered, and all these contributions are summed up taking into account the
oscillator strength of each individual exciton.

Figure 4: Imaginary part of the dielectric function calculated using a GW+BSE
approach for (a) monolayer and (b) AA′ bilayer WSe2 together with the
oscillator strengths of the main excitonic transitions. The shaded regions
correspond to the main peaks identified in experiments. (c,d) Weight in
reciprocal space of the transitions contributing to each of these peaks.

Peaks A and B are formed by transitions near $K$ and both features undergo a
blueshift in the bilayer due to an increase in the direct band gap at $K$
compared to that of the monolayer from. Peak C has a more complex structure
where k-points next to both $K$ and $Q$ contributes. While the points next to
$K$ have a higher spectral weight, the $Q$ points are three times more
numerous and thus the integrated contribution of the two regions of the
reciprocal space is comparable. Peak C had been previously described as a
higher-order exciton of the same series as peak B Hong  _et al._ (2021); Fig.
4(c,d) illustrates that additional excitonic transitions contribute to this
spectral feature. Finally, peak D is dominated by transitions from the last
occupied to the first unoccupied band near the $Q$ point. The appearance of
these strong high-energy excitonic transitions had been linked to the high
joint density of states that arises from band nesting effects in TMDCs
Carvalho  _et al._ (2013); Bieniek  _et al._ (2018); Mennel  _et al._ (2020).

As discussed in the experimental results, blueshifts of the excitonic peak C
in bilayer TMDCs were observed as a function of their twist angle by means of
EELS. It can be reasonably argued that the decomposition in the reciprocal
space of these spectral features might be the same for aligned and twisted
bilayers. Therefore, while the GW+BSE approach gets too computationally
expensive when applied to extended moiré supercells, it might be feasible to
link trends observed in the spectra to continuous changes of the DFT
electronic structure. The band structure of a moiré supercell is highly folded
and therefore it can be hardly compared to those of a reference untwisted
bilayer or monolayer without the use of unfolding methods which provide a
primitive cell effective band structure Ku  _et al._ (2010); Lee  _et al._
(2013). These techniques have been employed already for the unfolding of the
bands of various twisted 2D heterostructures Nishi  _et al._ (2017);
Matsushita  _et al._ (2017); Sánchez-Ochoa  _et al._ (2020); Magorrian  _et
al._ (2022). Unfolding routines require the definition of a reference
primitive basis; in the case of moiré structures, unfolding has to be
performed twice to take into account the intrinsic periodicity of each of the
two layers separately. The unfolded bands can then be projected independently
on the two layers used as reference.

As an example, in Fig. 5(a) the unfolding method has been applied to a moiré
supercell with a twist angle of 21.8∘ (computational details are provided in
the SI). The purple and blue bands in Fig. 5(c) were unfolded using the
primitive cell of the bottom and top layer as a reference [following their
color-coded cells in Fig. 5(b)], respectively, and were subsequently projected
onto the same layer. The $\Gamma$–$K$–$M$ path connects high symmetry points
of the bottom layer but not of the top layer. The unfolded occupied purple
bands closely follow those of the untwisted AA′ bilayer (white dashed lines),
although an upper shift is observed near the $\Gamma$ point. This region of
reciprocal space is where the occupied bands of the monolayer (yellow dashed
line) and bilayer differ and is very sensitive to the interlayer spacing
which, in twisted TMDCs, can vary with the twist angle and the layer registry
van der Zande  _et al._ (2014); Yan  _et al._ (2019). Finally, a few small
minigaps ($<50$ meV) open at the crossing of the bands of the two layers if
they present the same spin character. More relevant variations are observed in
the conduction bands. With respect to the AA′ bilayer, the first two
unoccupied bands of the monolayer cross in the $K$–$Q$ path and are higher in
energy at $M$. The unfolded purple bands show the same characteristics and can
therefore be reasonably interpreted as the bands of the monolayer perturbed by
the adjacent twisted layer.

Figure 5: (a) Structure of a WSe2 moiré supercell with a twist angle of 21.8∘.
Blue and purple arrows indicate the primitive unit cell of the top and bottom
layer, respectively. (b) Reciprocal lattice vectors and Brillouin zones of the
primitive cells of the individual layers, and the supercell Brillouin zones
(grey hexagons). (c) DFT band structure of the moiré cell unfolded along the
high symmetry directions of the irreducible Brillouin zone shaded in (b). Blue
and purple lines correspond to the unfolding using as reference primitive cell
those of the top and bottom layer, respectively. The yellow and white dashed
lines are the band structure of WSe2 monolayer and untwisted bilayer,
respectively. (d) Unfolded band structures of WSe2 moiré supercells with
different twist angles projected over the reference layer. (e) Direct and
indirect band gap as a function of the twist angle. Gray dashed line indicates
the direct gap of the WSe2 monolayer.

In Fig. 5(d) the unfolded bands of twisted WSe2 bilayers are presented as a
function of the twist angle. At the $K$ point, both the direct band gap and
the spin-orbit splitting do not change with the twist angle and remain equal
to the value of the untwisted bilayer [blue dots in Fig. 5(e)]. This behavior
can be explained by the reduced interlayer orbital coupling for the band-edge
states at the $K$ point Kang  _et al._ (2016). Reasonably assuming that layer
twists will only have a minor effect on screening, invariance of low-energy
excitonic features can be deduced, as observed for the A and B peaks in the
EELS spectra in Fig. 3(g).

While the valence band along the $K$–$Q$ path remains invariant with the twist
angle, the bottom of the CBM near $Q$ point upshifts. The values of the
indirect gap $KQ$ as a function of the twist angle were extracted and plotted
in Fig. 5(e). The maximum of the indirect gap occurs for twist angles close to
30∘ and progressively decreasing of several tens of meV going towards 0∘ and
60∘. This behavior reproduces well the trend observed in the indirect gap
measured by photoluminescence Merkl  _et al._ (2020) where $KQ$ indirect
excitons can be activated by phonons. C and D excitons observed in EELS,
optical absorption or reflectivity, involves dipolar transitions near the
$Q$-point. Since the upper valence band in this reciprocal space region is not
affected by the twist, the energy difference of the vertical transitions
follows the same trend as the indirect gap, but it is shifted at higher
energies. While the excitonic response of twisted WSe2 has not here been
explicitly calculated, the analysis of the unfolded bands combined with the
study of the excitonic character of spectral features from mono and bilayer
permits explaining the experimental trends observed in EELS as a function of
twist angle. It should be noted that this interpretative scheme may not be
valid when considering very low (near-zero) twist angles, where the moiré
structure undergoes extensive structural relaxations and where excitonic
states may re-hybridise Brem  _et al._ (2020), giving rise to complex spectral
features that cannot be simply linked to the spectral response of the perfect
mono- or bilayer.

##  IV Conclusions

The evolution of the excitonic response in twisted bilayer WSe2 as a function
of moiré angle has been investigated using monochromated STEM-EELS under
cryogenic conditions, highlighting a progressive blueshift of the high-energy
C excitonic peaks relative to the AA′-stacked bilayer. Atomically-resolved
imaging was used to provide relevant structural information on the twisted
bilayers, including the twist angle and stacking order, in addition to
revealing the occurrence of atomic reconstruction in the lowest observed twist
angle of 2.3∘. In combination with first-principles calculations based on the
GW+BSE approaches, the physical origin of the high-energy spectral features in
monolayer and AA′ bilayer WSe2 were examined. Moreover, the unfolded DFT
electronic structure of twisted bilayers showed an uplifting of the $Q$-valley
CBM with respect to the untwisted AA′ bilayer. The trends in band structure
changes with moiré angle were then linked to the BSE calculated dielectric
response of the untwisted bilayer, giving good agreement to the dipolar
transitions near $Q$-point contributing to the high-energy C exciton observed
in EELS from the current work as well as the phonon-assisted indirect $K$–$Q$
transition measured by photoluminescence by other groups. Therefore tuning of
the C exciton transitions as measured by absorption spectroscopy like EELS is
an effective indicator of the electronic interlayer coupling.

With capabilities to collect photons generated by cathodoluminescence within
the electron microscope utilised in this study, the addition of h-BN
encapsulation can bring further insight to interlayer interactions in such
twisted bilayers, in particular at the lengthscales of the moiré periodicity.
The expected reduction in EELS absorption linewidths will aid in the
identification of small spectral variations; in conjunction, sufficient
excitation of electron-hole pairs in the h-BN for recombination in the TMDC
opens the possibility for concurrent correlation to the indirect exciton
emission in the twisted bilayers.

###### Acknowledgements.

The authors acknowledge funding from the ANR, program of future investment
TEMPOS-CHROMATEM (No. ANR-10-EQPX-50). This work was supported by the European
Union in the Horizon 2020 Framework Program (H2020-EU) under Grant Agreement
No. 823717 (ESTEEM3) and 101017720 (eBEAM). S.Y.W. acknowledges NSERC for the
postdoctoral fellowship funding. A.A. acknowledges financial support from the
German Research Foundation (DFG Project Nos. AR 1128/1-1 and AR 1128/1-2) and
and NM-ICPS of the DST, Government of India through the I-HUB Quantum
Technology Foundation (Pune, India). M.P. acknowledges CINECA for CPU time
granted within ISCRA-B and ISCRA-C initiatives. This research was sponsored
[in part] by the NATO Science for Peace and Security Programme under grant
G5936.

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##  I Supporting Information

###  S1 STEM Characterization

The atomically-resolved imaging has been performed on an aberration-corrected
Nion UltraSTEM200 operated at 60 keV using a convergence angle of 33 mrad with
typical beam currents of 20 – 40 pA and a detector angular range of 80 – 200
mrad for the high-angle annular dark-field (HAADF) images. Additional low-
angle annular dark-field (LAADF), which mimicks the diffraction contrast of
dark-field TEM for twisted bilayer graphene domains Brown  _et al._ (2012);
Yoo  _et al._ (2019) was carried out using 10 mrad convergence angle and 14 –
28 mrad detector angular range. The monochromated EELS and electron
diffraction were performed on a modified Nion HERMES-S200 (also known as
ChromaTEM) operated at 60 keV with the sample at liquid nitrogen temperatures
(T $\approx$ 150 K) using 10 – 15 mrad and 3 – 5 mrad convergence angle,
respectively. STEM-HAADF image simulations were performed using a multislice
simulation package called QSTEM,Koch (2002) with the input parameters
(convergence angle, HAADF detector collection angles) set according to the
experimental conditions. The EELS spectra were processed using a combination
of Gatan DigitalMicrograph, and the python package HyperSpy.de la Peña  _et
al._ (2020) Energy positions of exciton peaks were extracted by Gaussian peak
fitting the negative of the second-derivative of smoothed spectra obtained by
Savitzky-Golay filtering. All spectra displayed are in their raw state, summed
over tens of nanometer regions with a power-law background subtracted.

###  S2 Computational Details

#### Spectra simulations

The mono- and bi-layer WSe2 structures have been optimized using the Quantum
Espresso codeGiannozzi  _et al._ (2009) using the Perdew-Burke-Ernzerhof (PBE)
exchange-correlation functionalPerdew  _et al._ (1996) and optimized norm-
conserving pseudopotentials.Hamann (2013) Dispersion forces have been
introduced to relax both the mono and bilayer by the vdW revised-DF2 method
which has been demonstrated to be accurate for 2D materials.Hamada (2014) DFT
eigenvalues and eigenfunctions have been used in successive many-body
simulations performed using the YAMBO code.Marini  _et al._ (2009); Sangalli
_et al._ (2019) Quasi-particle energies have been calculated via a GW
perturbative one-shot method and optical excitation energies and optical
spectra by solving the Bethe-Salpeter Equation. Further details on the
computational parameters employed both in DFT and many-body calculations can
be found in Ref. 11, appendix D. The analysis of the weight in the reciprocal
space of the optical transitions contributing to the excitons has been later
performed using the yambopy post-processing package.Sangalli  _et al._ (2019)

#### Band unfolding

DFT calculations in the band unfolding part have been performed using the
OpenMX code.Ozaki (2003) The exchange-correlation potential was expressed in
the generalized gradient approximation using the Perdew-Burke-Ernzerhof (PBE)
schema. Norm conserving full relativistic pseudopotentials including a partial
core correction and spin-orbit coupling was used. Dispersion interactions have
been included using the Grimme-D2 method.Grimme  _et al._ (2010) A basis set
of optimized numerical pseudo-atomic orbitals was employed as provided by the
openmx 2019 database.

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Figure S1:  (a) Low-magnification STEM bright-field (BF) image of WSe2 layers
with many holes marked containing few hundred nanometer-sized fragments of
bilayers (BLs) and trilayers (TLs) with variable twist angles, including that
in (b) with 3.4∘ twist angle measured from the image fast-Fourier transform.
(c) Monochromated EELS spectra from twisted bilayers with various moiré angles
compared to a representative monolayer (ML). (d) Example of the fitting
procedure of the exciton peak positions by Gaussian peak fitting of the
$-d^{2}I/dE^{2}$ on Savitzky-Golay filtered spectra. (e) Graphical
representation of the transition energy difference between select exciton
resonances as a function of twist angle from the peak fitting. Figure S2: (a)
STEM bright-field (BF) images of WSe2 layers from holes marked in Fig. S1
following the same color-code. (b) Monochromated EELS spectra from twisted BLs
and TLs with various moiré angles, including 0∘, compared to a representative
ML. STEM-HAADF images of the WSe2 layers from the holes marked in red (c,d),
green (e,f) and blue (g,h) from the BF images in (a), highlighting the
different moiré periodicity with twist angle in the high-magnification images
in (d,f,h). Figure S3: (a) Low-magnification STEM bright-field (BF) image of
WSe2 layers including the two BL regions with high twist-angle formed by
folding from Fig. 3(a). STEM-HAADF images of the WSe2 layers with (b–c) 13∘
and (f–g) 28∘ twist angle from the regions marked correspondingly in the BF
image in (a). The image FFT with the assessment of the folding orientation
(black dashed line) and its normal direction (green arrow) with respect to the
closest armchair and zig-zag directions (dotted lines) of the underlying ML
for (d) 13∘, and (h) 28∘ twist angles. The red-dashed hexagon marks the
orientation of the underlying ML, and the blue marks the orientation of the
additional layer. The second-order reflection (armchair) circled in red
indicates that for both angles, the folding normal lies towards an armchair
direction, such that this would result in AA (or R-type) stacking in the BL.
(e) Schematic illustration of how folding of MLs along a zig-zag (zz)
direction [top] such as in Fig. 2, or in between zig-zag (zz) and ac
directions [bottom] can result in BLs of zero or non-zero twist angles,
respectively. Figure S4: Low-magnification STEM-HAADF images of WSe2
containing BLs and TLs with (a) 2.3∘, (e) 3.4∘ and (f) 5.5∘ twist angle. (b)
Convergence and collection angle geometry for the STEM-LAADF imaging overlaid
over a WSe2 ML diffraction pattern used to observe the contrast at the bridge
(Br) separating the AB/BA domains in the moiré unit cell. STEM-LAADF images of
the WSe2 BLs with low twist angles of (c) 2.3∘, (g) 3.4∘ and (i) 5.5∘, showing
arrays of six-fold triangular domains that indicate AA-type stacking and
atomic-reconstruction. Corresponding STEM-HAADF images of WSe2 BLs with (d)
2.3∘ and (h) 3.4∘ twist angle from the regions marked in the LAADF images in
(c) and (g), respectively. Figure S5: Orbital-projected band structures of AA′
stacked bilayer WSe2 highlighting the orbital character separately for (a) the
W atom $5d$ orbitals, and (b) the Se atom $4p$ orbitals. Symbol size is
proportional to its orbital weight.


# Template-based Recruitment Email Generation for Job Recommendation

Qiuchi Li  Christina Lioma
Department of Computer Science
University of Copenhagen
{qiuchi.li<EMAIL_ADDRESS>
###### Abstract

Text generation has long been a popular research topic in NLP. However, the
task of generating recruitment emails from recruiters to candidates in the job
recommendation scenario has received little attention by the research
community. This work aims at defining the topic of automatic email generation
for job recommendation, identifying the challenges, and providing a baseline
template-based solution for Danish jobs. Evaluation by human experts shows
that our method is effective. We wrap up by discussing the future research
directions for better solving this task.

##  1 Introduction and Prior Work

Recruitment email generation is a crucial step in the overall job recruitment
process. When recruiters find suitable candidates for a job, they need to
write emails to contact the candidates, explaining why they are fit for the
job and inviting them to apply. The job market produces a huge number of job
postings on a daily basis, which results in a significant amount of human
labor required to write recruitment emails. The recruiters are in urgent need
of an automatic approach to compose these emails, to reduce their efforts and
increase productivity.

The core challenges facing the task are two-fold: persuasiveness and
personalization. First, the emails should contain sufficient reasons to
convince the candidate that their qualifications match the requirements of the
position, and illustrate that the position meets the candidate’s expectations.
Second, personalized emails should let the job seekers feel that the
recruiters pay special attention to them and in turn motivate them to apply.
In an interview of recruiters of varying recruitment experience, recruiters
believe that proper personalization on the recruitment emails could boost
candidate positive response rate, and admit that the current emails, largely
composed based on fixed templates, are not personalized and persuasive enough
Bogers and Kaya (2021).

Recruitment email generation can be seen as a task-oriented text generation
problem. One needs to extract information from the input job description and
candidate profile, and generate the recruitment email accordingly. A large
body of research on emails focuses on the analysis side, typically automatic
detection of phishing emails by extracting features Basnet et al. (2008);
Verma and El Aassal (2017); Yu et al. (2009). In the realm of email synthesis,
email subject line generation Xue et al. (2020); Zhang and Tetreault (2019)
has been studied. For generation of the main email body, attempts have been
made on fake email generation for malicious purposes Das and Verma (2019);
Baki et al. (2017) based on a two-step pipeline Chen and Rudnicky (2014a, b)
for email synthesis. As far as we know, generating recruitment emails in job
recruitment is an unexplored task in literature, despite its practical
significance and challenging nature.

We therefore formally formulate the task of recruitment email generation and
provide a baseline template-based system for it by extending Chen and Rudnicky
(2014a)’s approach. In particular, we randomly generate an email from a
library of different pre-defined components, and fill the motivational
sentence with certain slots by combining matched skills and occupations
extracted from the job and candidate in question. We conduct a user study to
evaluate the quality of the generated text and examine if it can save
recruiters’ time in writing emails. The results show that recruiters under
test are overall satisfied with the generated emails, and the time spent on
writing emails for each candidate is significantly reduced.

The rest of the paper is organized as follows. We define the problem formally
in Section 2. We run a pilot study to examine the simple end-to-end neural
generation algorithm in Section 3 and elicit the need for a fine-grained
synthesizing approach. Our template-based approach is described in Section 4,
and the user evaluation is reported and discussed in Section 5. We conclude
and point out future directions in Section 6.

##  2 Task Definition and Notations

Recruitment email generation calls for generating an email based on an input
the job and a candidate . It requires us to extract the matched information
from different job and candidate fields, and convert it to natural language
expressions in the email. Typically, a job posting contains its title, company
name and a textual description. A candidate profile has a headline, a list of
keywords, a list of preferred job titles, the candidate’s work experience and
educational experience, and the candidate’s resume text.

Figure 1: Example of a real recruitment email for the given job and candidate
pair. The texts are translated from Danish to English.

The data we work with comes from Jobindex111https://www.jobindex.dk/, one of
Scandinavia’s largest job portals and recruitment agencies. In the Jobindex
system, the above-mentioned fields are the main source of information for jobs
and candidates. The majority of the jobs and candidate profiles are in Danish.
Please refer to Fig. 1 for an example of a pair of job and candidate, and the
real recruitment email written by a human recruiter. The personal information
has been removed from the example and all texts are translated to English.

##  3 Pilot Study: end-to-end neural generation

The most intuitive way to handle the recruitment email generation task is to
generate the whole email in a fully end-to-end fashion by concatenating the
job and company text. We conduct a pilot study to examine whether this setting
works in practice.

Model. We build a Transformer encoder-decoder, the state-of-the-art
architecture for natural language generation, to support a sequence-to-
sequence generation of recruitment email. The job and candidate texts are fed
to the encoder side, and the recruitment email is generated in a token-by-
token auto-regressive manner. The Transformer structure enables us to load the
weights of the pre-trained Danish language model, Danish-BERT
222https://huggingface.co/Maltehb/danish-bert-botxo. Rothe et al. (2020)
conducted a comprehensive comparison of different strategies to use the pre-
trained BERT weights for sequence-to-sequence generation, and found that the
bert2bert setting can yield robust performance across different text
generation tasks. Therefore, we follow this setting and initialize the weights
for both encoder and decoder with the Danish-BERT checkpoint. Please refer to
Rothe et al. (2020) for further details. We claim that this model may not be
the state-of-the-art for Danish text generation, since Danish GPT is publicly
available 333https://huggingface.co/flax-community/dansk-gpt-wiki. However, it
is a strong-performing model due to its Transformer architecture, and its
generated text is representative of the state-of-the-art neural generation
models.

Data Preprocessing. We focus on generating Danish emails from Danish jobs and
candidate profiles. We mine (job, candidate, email) triplets from the Jobindex
database. We filter out samples with non-Danish text, too short job
descriptions and empty job titles. A total number of 317566 samples are
obtained. Due to the limitation on input length by the model, we concatenate
the summary of job description and the headline, job titles, education
experience and work experience of candidate. Different fields are split by the
periods. For recruitment emails, we only take their main bodies. We replace
specific job title and company name with special tokens “[job]” and “[cpy]”
respectively.

Training and Evaluation. Our Transformer encoder-decoder has 12 layers and 12
attention heads and a maximum length of 512 tokens on both encoder and decoder
sides. The embedding dimension and hidden dimension are set to 768 and 3072,
respectively. We split all samples into training, validation and test set at a
7:2:1 ratio. We perform mini-batch learning to train the model with a batch
size of 32. The average cross-entropy loss over all tokens at the decoder side
is used as the loss function. We train the model on the training set for a
maximum number of 5 epochs with Adam optimizer, and stop training when the
validation loss stops dropping.

We use bilingual evaluation understudy (BLEU) as the quantitative metric for
the generated message. The BLEU scores are calculated for individual
translated segments, by comparing them with a set of good quality reference
translations. The average scores over the whole test corpus are computed as an
estimation of the overall quality.

Result. We obtained a BLEU score of 23.2 on test samples for the generated
texts, which is far from the SOTA neural text generation system for Danish
(32.5 - 33.8) Fan and Gardent (2020). This reveals that the generated emails
have a limited overall quality.

Figure 2: Examples of generated emails by the end-to-end neural model. The
generated motivational sentences are translated in English and colored in red.

Additionally, manual inspection shows that the auto-generated emails have low
extent of personalization and persuasiveness. The generated motivational
sentences usually do not contain specific reasons of match and are prone to
grammatical errors. See Fig. 2 for concrete examples. We posit that it is
because the motivational sentences are usually located in the middle of email
body, embedded in texts primarily generated from templates. Also, over 80% of
the motivational sentences are devoid of case-specific information such as the
matched skills or occupations. Therefore, an end-to-end generation system is
capable of learning these recurring template-based texts quite well, but poor
at learning case-specific motivational sentences.

##  4 Proposed Methodology

The evaluation result of end-to-end neural generation indicates the need for a
finer-grained generation system. Instead of generating the email as a whole, a
special module should be developed to generate case-specific information, such
as the motivational sentence, from the input job and candidate texts. We are
inspired by the two-step email synthesis Chen and Rudnicky (2014a) to generate
recruitment emails: email structure generation and slot filling. Specifically,
we randomly generate an email template from a pre-constructed library of email
components. The slots of the template indicate case-specific fields, and are
then filled by extracting information from the matched job and candidate. The
overall process of the system is shown in Fig. 3.

Figure 3: Diagram of the email generation system. The final generated messages
are the main body of a real recruitment email, translated to English for this
example.

Potentially, our approach is superior to end-to-end generation in three
aspects: 1) recruiters follow a similar process to write emails in a real-case
scenario, so the generated emails are more likely to be accepted by and
benefit recruiters; 2) the algorithm has a better control of the generated
content, and ensures a certain extent of grammatical correctness and
readability; 3) by explicitly composing case-specific information, the
algorithm enhances personalization and persuasiveness of the generated
recruitment emails.

In the following text, we introduce the implementation details of our
template-based generation system.

###  4.1 Template Parsing

Template data cleaning. We get a total number of 553 raw templates from
Jobindex system. We remove the non-Danish templates and templates for other
purposes from the collection, and the remaining ones are classified into
company-specific and general templates. Company-specific templates are
provided by some companies to the recruiters with the request that these
templates are strictly used when generating recruitment emails for these
specific companies. General templates are used for all other cases.
Eventually, 270 general templates and 81 specific templates remain in the
database.

Manual annotation. We define a list of email components (or fields) according
to their functions based on manual inspection. They are mainly categorized
into functional, case-specific and auto-fill fields. Functional fields are
indicative of email structure, and do not entail case-specific information.
Case-specific fields contain information specific to the matched pair of job
and candidate, such as the job title, company name, motivational sentences
expressing why the candidate is fit for the job, and so on. Some case-specific
fields are automatically filled by the Jobindex system, and they are referred
to as auto-fill fields instead. A total number of 54 email components are
defined.

For each template in the database, we manually annotate the text by inserting
HTML tags before and after a certain text segment to indicate its function in
the email.

Parsing. We parse the annotated templates to construct the email component
library, essentially a dictionary of (component_name, content list) pairs. The
annotated templates are each parsed in a recursive fashion by an HTML parser.
Starting from the root element of the whole template, the parser performs the
same operations for each element: identify its child elements, process each
the child element, append the processed child element content to the
dictionary, and replace the child element with the marker “[% f %]”. As such,
the parser navigates all HTML tags in the template and adds their contents to
the corresponding component content list. In the text content of each
component, its child components are all masked with “[% f %]” tags as desired.
The output of the algorithm is the top-level structure of the email templates,
which are stored in the dictionary as values of “skeleton_follower” and
“skeleton_non_follower” for followers and for non-followers, respectively.
Followers are candidates who follow a company in their profile, and non-
followers are candidates who do not follow a company. After parsing all
templates, we obtain a list of contents for each pre-defined email component.
Examples of extracted email components are shown in Fig. 4. It is worth noting
that nested structures universally appear among functional components, i.e.
the textual content of one component may contain other components.

Figure 4: The HTML tag, description and example of email components, extracted
from the template database.

###  4.2 Baseline Template-based Email Generation Algorithm

We build an email generation system based on the constructed email component
library. The algorithm randomly generates a template from the constructed
library, based on whether the database contains the company name and whether
the candidate is a follower of the company. Then, we extract the matched
skills and occupations from the input job and candidate, and convert them to a
coherent expression for the motivational sentence explaining why the candidate
fits the job.

Random template generation. Two issues should be accounted for when generating
a proper template for a matched (job, candidate) pair. First, some companies
prefer writing recruitment emails based on company-specific templates, so the
system should always use a company template where possible. Second, followers
should be contacted with slightly different emails to stress that the
candidate follows the company.

The algorithm works as follows. First, we search for templates with the
company name of the job posting. If company-specific templates can be found,
then we randomly choose a template from the matched company templates. If it
returns an empty list, then we randomly generate a template from the email
component library. We start with a randomly selected follower skeleton or a
non-follower skeleton according to whether the company is in the candidate’s
following list. Then, we navigate through all unfilled slots in an iterative
fashion. For each slot, we randomly pick a text content from all its
respective candidates in the library. Since the text expression may contain
other unfilled slots, this process goes in an iterative fashion until no
functional component tags appear in the templates.

To this end, we have obtained a template for the recruitment email. An example
can be seen in Fig. 5.

Figure 5: A randomly generated template for non-followers.

Template filling. We compose the motivational sentence based on the matched
qualifications of the candidate, including skills and occupations. For this
purpose, we construct a dictionary of skills and occupations from the ESCO
(European Skills, Competences, Qualifications and Occupations)
database444https://esco.ec.europa.eu/. ESCO has a 4-level category annotation
for skills and occupations in different languages. We include both skills and
occupations as dictionary keys in English and Danish. We also add the list of
IT-related skills from Microsoft Skills
library555https://learn.microsoft.com/en-us/ into the dictionary. A total
number of 129474 skills and 46060 occupations are collected.

Based on the dictionary, we extract skills and occupations from the matched
pair of job and candidate texts and obtain their intersections. We first use a
built-in name entity recognition (NER) algorithm in the
spaCy666https://spacy.io/ package to identify text spans that may contain
skills and occupations, and check whether each is a skill or an occupation.
The matched skills and occupations are obtained by taking the set
intersections. We further split the language skills from the matched skills
set.

Simple rules are then applied to convert the matched skills, occupations and
language skills to a coherent expression as explanations of why the candidate
fits the job. The skills and occupations are respectively concatenated with
the “{}, {} og {}” pattern, and conjunctive expressions are added to combine
the expressions for skill and occupation match. It is further appended by the
language skill match expression, if non-Danish skills are detected in both the
job and candidate. The whole expression is inserted to the motivation sentence
template with modifications of the pre-slot expressions to ensure coherence.
When multiple slots appear in a motivational sentence template, they are
inserted with matched skills and matched occupations, respectively. As a back-
up, a general text expression will be randomly chosen from a list of contents
in case of no matched qualifications.

Please see Fig. 6 for a visual illustration of how the matched qualifications
are converted to a motivational expression.

Figure 6: An example of composed motivational sentence from matched skills,
occupations and language skills.

Text post-processing. We automatically post-process the generated email in the
final step. Specifically, if multiple punctuations appear in a row, we keep
only the last punctuation. We also make sure the first letter of a sentence is
upper-cased. Finally, we correct the spacing errors between words in a
sentence, between sentences, and between paragraphs. This gives the final
output message produced by the template-based recruitment email generation
algorithm.

##  5 Evaluation

(a) Unhelped case.

(b) Helped case.

Figure 7: Screenshots of the interface in (a) “unhelped” and (b) “helped”
case.

We conduct an offline evaluation with real expert recruiters to evaluate the
performance of the template-based email generation algorithm. The quantitative
measures for natural language generation are not employed, since the template-
based approach may produce significantly different structures from the real
recruitment emails and still be reasonably good. Indeed, it remains an open
question to design quantitative measures for evaluating the generated
recruitment emails, as the judgments on the persuasiveness or personalization
of the email vary from person to person.

We randomly sample a collection of truly matched (job, candidate) pairs from
the Jobindex database. They are randomly assigned to each of a group of 10
recruiters. The pairs are randomly split into 5 sessions of 10 pairs each.
Each recruiter is randomly assigned with “helped” tasks (with generated texts)
and “unhelped” tasks (with generated templates). Each task is “helped” for
half of the recruiters and “unhelped” for the other half. The interface
contained pairs of matched job postings and candidate profiles, and an
interface is provided to the recruiter with either pre-generated template
(“unhelped”) or the email (“helped”). For the email, the inserted case-
specific information is wrapped by brackets “{}”. A checkbox is also presented
to the recruiters for providing a 4-level judgment on the quality of the email
or template (4 = Perfect, 3 = Minor Revision, 2 = Major Revision, 1 =
Useless). The judgment is on language quality for templates and on both
language quality and information correctness for emails. The recruiters are
asked to click a button before and after writing the recruitment email, so
that the difference between the recorded time stamps are the time spent on it.
The screenshots of the interface are shown in Fig. 7.

Result on text quality. We have collected the results of 224 helped tasks
(with generated emails by our algorithm) and 241 unhelped tasks (with our
generated templates by our algorithm). The recruiters’ annotations on the
quality of the generated texts are shown in Fig. 8. In terms of average
ratings, we obtained a value of 2.53 for generated emails and 2.46 for
generated templates for the evaluation of the new template-based system. Over
50% of the generated texts (53.58% for emails and 51.03% for templates) are
satisfactory, requiring minor or no edits. It is interesting to see that more
recruiters are satisfied with the generated texts than the templates. This
implies that the generated motivational sentence is exceptionally helpful for
recruiters. In the same time, there is still room for the text quality to
improve, and neural method for generating case-specific information is a
feasible direction.

Figure 8: Bar plots of annotations for the offline-evaluation. The red bars
are the recruiters’ judgments on the quality of generated templates, and blue
bars are the recruiters on the quality of generated messages.

Result on time cost. We computed the average time a recruiter spent on writing
the message in the unhelped and helped case, respectively. The time difference
between the two scenarios is the reduction of recruiters’ time by our
template-based automatic text generation system. As a consequence, recruiters
spent an average of 178.99s on each matched pair in the helped case, and an
average of 235.63s on each matched pair in the unhelped case. On average, a
recruiter spends 56.64s shorter on writing an email with automatically
generated messages than merely on top of the provided templates. In order to
remove the discrepancy across individual recruiters, we zero-averaged the time
for each recruiter and recalculated the time difference. A closer but still
remarkable gap between the times (51.31s) is observed. It shows that this
simple template-based system can significantly save recruiters time on writing
recruitment emails.

##  6 Conclusion

We have proposed recruitment email generation for job recruitment, a novel
task in text generation. We demonstrate its challenge and significance in the
pilot study and a user study of our template-based approach. The challenge
mainly resides in constructing case-specific components of the email from the
input job and candidate to enhance personalization and persuasiveness. Quite
often, the reasons of a good match are semantically non-explicit and cannot be
extracted by common word or phrase matching. At the same time, we have
observed that a simple approach can remarkably benefit recruiters by saving
around 1/4of their time.

Future work could contribute to this task in the following aspects:

  * •

End-to-end neural generation of motivational sentences. Rather than the simple
rule-based algorithm for composing the motivational sentences, we may learn a
neural generation system from the real emails in a data-driven manner.

  * •

Robust intrinsic evaluation metrics. The n-gram matching-based metrics, such
as BLEU or ROUGE, are not suitable for this task. Beyond the real-user
evaluation, it remains an open challenge to propose robust metrics that
objectively evaluate the generated text, in terms of both language quality and
task-related aspects.

  * •

Deep representations of job and candidate to better extract reasons of match.
Deep neural models should be constructed to perform deep understanding of jobs
and candidates in order to support extraction of below-the-surface matched
qualifications.

  * •

Generating explanation of recommendations. Currently, the email generation is
a separate process from job recommendation. It would be interesting to view
recruitment emails instead as explanations of the recommendation systems, and
propose recommendation models that supports interpretations of the
recommendations. The authentic explanations of recommendation will be then be
incorporated in the recruitment email.

## Acknowledgements

This research was supported by the Innovation Fund Denmark, grant no.
0175-000005B.

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