vocabulary size of 37,069 unique tokens.

####  5.2.2 Evaluation Metrics

##### Goal-Oriented

For both MWOZ and SMD, we follow the evaluation done by existing works [57,
73, 58] and [67]. We use the BLEU333Using the multi-bleu.perl script score
[221] to measure the response fluency and Entity F1-Score [71, 73] to evaluate
the ability of the model to generate relevant entities from the dynamic
memory. Since MWOZ also includes SQL and BOOK queries, we compute the exact
match accuracy ($ACC_{SQL}$ and $ACC_{BOOK}$) and BLEU score ($BLEU_{SQL}$ and
$BLEU_{BOOK}$). Furthermore, we also report the F1-score for each domain in
both MWOZ and SMD.

##### Chit-Chat

We compare the perplexity, BLEU score, F1-score [111], and consistency score
of the generated sentences with the human-generated prediction. The
consistency score [112] is computed using a Natural Language Inference (NLI)
model trained on dialogue NLI [222], a recently proposed corpus based on the
Persona dataset. We fine-tune a pre-trained BERT model [99] using the dialogue
DNLI corpus and achieve a test set accuracy of 88.43%, which is similar to the
best-reported model in [222]. The consistency score is defined as follows:

$\displaystyle\text{{NLI}}(u,p_{j})=\Bigg{\\{}\begin{array}[]{rcr}1&\text{if
$u$ entails $p_{j}$}\\\ 0&\text{if $u$ is independent to $p_{j}$}\\\
-1&\text{if $u$ contradicts $p_{j}$}\\\ \end{array}$ (5.12)
$\displaystyle\text{{{C}}}(u)=\sum_{j}^{m}\text{{NLI}}(u,p_{j}),$ (5.13)

where $u$ is a generated utterance and $p_{j}$ is one sentence in the persona
description. In [222] and [112], the authors showed that by re-ranking the
beam search hypothesis using the DNLI score (i.e., C score), they achieved a
substantial improvement in dialogue consistency. Intuitively, having a higher
consistency C score means having a more persona-consistent dialogue response.

####  5.2.3 Baselines

In our experiments, we compare Sequence-to-Sequence (Seq2Seq) [223],
Transformer (TRS) [97], Mixture of Experts (MoE) [170] and Attention over
Representation (AoR) with our proposed Attention over Parameters (AoP). In all
the models, we used the same copy-mechanism as in [223]. In AoR, instead of
mixing the parameters as in Equation 5.3, we mix the output representation of
each Transformer decoder (i.e. Equation 2.29). For all AoP, AoR, and MoE,
$r=13$ is the number of decoders (experts): two skills for SQL and BOOK and 10
different domains for MWOZ+SMD and one for Persona-Chat. Furthermore, we also
include the following experimental models: AoP using the gold attention vector
$V$, which we refer to as AoP w/ Oracle (or AoP \+ O); AoP trained by removing
the $L_{\theta^{*}}(V)$ from the optimization, which we refer to as AoP w/o
$L_{\theta^{*}}(V)$; and as previously mentioned, the Universal Transformer
for both AoP, which we call AoP + U, and the standard Transformer, which we
call TRS + U (six hops). Detailed descriptions of all models and the full set
of hyper-parameters used in the experiments are reported in Appendix C.

####  5.2.4 Results

Table 5.3 and Table 5.2 show the respective evaluation results on the MWOZ+SMD
and Persona-Chat datasets. From Table 5.3, we can identify three patterns 1)
AoP and AoR perform consistently better then the other baselines, which shows
the effectiveness of combining parameters using the correct prior $V$; 2) AoP
performs consistently, but marginally, better than AoR, with the advantage of
an algorithmically faster inference and 3) using Oracle (AoP+O) gives the
highest performance in all measures, which shows the performance upper-bound
for AoP.

Model | Ppl. | F1 | C | BLEU
---|---|---|---|---
Seq2Seq | 39.42 | 6.33 | 0.11 | 2.79
TRS | 43.12 | 7.00 | 0.07 | 2.56
MoE | 38.63 | 7.33 | 0.19 | 2.92
AoR | 40.18 | 6.66 | 0.12 | 2.69
AoP | 39.14 | 7.00 | 0.21 | 3.06
TRS + U | 43.04 | 7.33 | 0.15 | 2.66
AoP + U | 37.40 | 7.00 | 0.29 | 3.22
AoP w/o $L_{\theta^{*}}(V)$ | 42.81 | 6.66 | 0.12 | 2.85
AoP + O | 40.16 | 7.33 | 0.21 | 2.91
Figure 5.2: Results for the Persona-Chat dataset.

Hence, the performance gap when not using Oracle attention is most likely due
to the error in attention $\alpha$ (2% error rate). Moreover, Table 5.3 shows
that by removing $L_{\theta^{*}}(V)$ (AoP w/o $L_{\theta^{*}}(V)$) the model
performance decreases, which confirms that good inductive bias is important
for learning how to select and combine parameters (experts). Additionally, in
Appendix D and E, we report the per-domain F1-score for SQL, BOOK and
sentences, and Table 5.2 and Table 5.3 with the standard deviation among the
three runs.

Furthermore, from Table 5.2, we can see that MoE has the lowest perplexity and
F1-score, but AoP has the highest consistency and BLEU score. Note that the
perplexity reported in [110] is lower since the vocabulary used in their
experiments was smaller. In general, the difference in performance among the
models is marginal, except for the consistency score; thus, we can conclude
that all the models can learn this skill reasonably well. Consistent with the
previous results, when $L_{\theta^{*}}(V)$ is removed from the optimization,
the models’ performance decreases.

Model | F1 | BLEU | SQLAcc | SQLBLEU | BOOKAcc | BOOKBLEU
---|---|---|---|---|---|---
Seq2Seq | 38.37 | 9.42 | 49.97 | 81.75 | 39.05 | 79.00
TRS | 36.91 | 9.92 | 61.96 | 89.08 | 46.51 | 78.41
MoE | 38.64 | 9.47 | 53.60 | 85.38 | 37.23 | 78.55
AoR | 40.36 | 10.66 | 69.39 | 90.64 | 52.15 | 81.15
AoP | 42.26 | 11.14 | 71.1 | 90.90 | 56.31 | 84.08
TRS + U | 39.39 | 9.29 | 61.80 | 89.70 | 50.16 | 79.05
AoP + U | 44.04 | 11.26 | 74.83 | 91.90 | 56.37 | 84.15
AoP w/o $L_{\theta^{*}}(V)$ | 38.50 | 10.50 | 61.47 | 88.28 | 52.61 | 80.34
AoP+O | 46.36 | 11.99 | 73.41 | 93.81 | 56.18 | 86.42

Table 5.3: Results for the goal-oriented responses in both MWOZ and SMD. The
last raw uses the Oracle, and bold-faced are best in each setting (w and w/o
Universal). Results are averaged among three run (full table in Appendix F).

Finally, in both Table 5.2 and Table 5.3, we report the results obtained by
using the Universal Transformer, for both AoP and the Transformer. By adding
the layer recursion, both models are able to consistently improve on all the
evaluated measures, in both Persona-Chat and the Task-Oriented tasks. AoP, in
particular, achieves better performance than Oracle (single layer) on SQL
accuracy, and has a consistently better performance in the Persona-Chat
evaluation.

Table 5.4: Selecting different skills thought the attention vector $\alpha$
results in a skill-consistent response. AoP response activates SQL and Train.

###  5.3 Skill Composition

To demonstrate the effectiveness of our model in learning independent skills
and composing them together, we manually trigger skills by modifying $\alpha$
and generate 14 different responses for the same input dialogue context. This
experiment allows us to verify whether the model accurately captures the
meaning of each skill and whether it can properly learn to compose the
selected parameters (skills). Table 5.4 first shows the dialogue history along
with the response of AoP on the top, and then different responses generated by
modifying $\alpha$ (black cells correspond to 1 in the vector, while the white
ones are 0). By analyzing Table 5.4, we can notice that:

  * •

The model learns the correct semantics of each skill. For instance, the AoP
response is of type SQL and Train, and by deactivating the SQL skill and
activating other domain-skills, including Train, we can see that the responses
are grammatical and they are coherent with the selected skill semantics. For
instance, by just selecting Train, the generated answer becomes “what time
would you like to leave?”, which is coherent with the dialogue context since
such information has not been yet provided. Interestingly, when the Persona
skill is selected, the generated response is conversational and also coherent
with the dialogue, even though it is less fluent.

  * •

The model effectively learns how to compose multiple skills. For instance,
when SQL or BOOK is triggered the response produces the correct SQL syntax
(e.g. “SELECT * FROM …” etc.). By also adding the corresponding domain-skill,
the model generates the correct query format and attributes relative to the
domain type (e.g., in SQL, Restaurant, the model queries with the relevant
attribute food for restaurants).

Figure 5.3: AoP visualization, vector $\alpha$ for different reference, (Ref.)
and AoP, generated answers. Top rows (Usr) are the last utterances from each
dialogue context.

###  5.4 Attention Visualization

Figure 5.3 shows the attention vector $\alpha$ over parameters for different
generated sentences. Note that the sentences shown in the figure are the
expected responses, not the given input, which is omitted since it can be very
large. In this figure, and by analyzing more examples, we can identify two
patterns:

  * •

AoP learns to focus on the correct skills (i.e., SQL, BOOK) when API-calls are
needed. From the first example in Figure 5.3, we can see that the activations
in $\alpha$ are consistent with those in the correct attention vector $P$.
There are also false positives, in which AoP puts too high a weight on BOOK
when the correct response is plain text that should request more information
from the user (i can help you with that. when would you like to leave the
hotel?). However, we can see that this example is, in fact, "almost correct"
as triggering a booking API-call may also be considered a valid response.
Meanwhile, the third example also fails to attend to the correct skill, but,
in fact, generates a very fluent and relevant response. This is most likely
because the answer is simple and generic.

  * •

The attention often focuses on multiple skills not directly relevant to the
task. We observe this pattern especially when there are other skill-related
entities mentioned in the context or the response. For example, in the second
dialogue example in Figure 5.3, we notice that AoP not only accurately focuses
on the taxi domain, but also has non-negligible activations for restaurant and
hotel. This is because the words “hotel" and “restaurant" are both mentioned
in the dialogue context and the model has to produce two entities of the same
type (finches bed and breakfast and ask).

###  5.5 Short Summary

We propose a novel way to train a single end-to-end dialogue model with
multiple composable and interpretable skills. Unlike previous work, which has
mostly focused on the representation-level mixing [170], our proposed
approach, Attention over Parameters, learns how to softly combine independent
sets of specialized parameters (making an SQL-Query, conversing with a
consistent persona, etc.) into a single set of parameters. By doing so, we not
only achieve compositionality and interpretability but also gain an
algorithmically faster inference speed. To train and evaluate our model, we
organize a multi-domain task-oriented dataset into end-to-end trainable
formats and combine it with a conversational dataset (Persona-Chat). Our model
learns to consider each task and domain as separate skills that can be
composed with each other, or used independently, and we verify the
effectiveness of the interpretability and compositionality with competitive
experimental results and thorough analysis.

##  Chapter 6 Conclusion

In this thesis, we focused on the controllability of deep learning-based, end-
to-end, generative dialogue systems in both task-oriented and chit-chat
scenarios. In particular, we explored the different aspects of controlling
generative dialogue systems: we described how to control the style and topics
of chit-chat models, how to continuously control and extend task-oriented
dialogue systems, and how to compose and control multi-skill dialogue models.

In controlling the style and topics of generative chit-chat dialogue systems,
we first proposed and evaluated plug-and-play methods for controllable
response generation, which do not require dialogue specific datasets nor rely
on fine-tuning a large model. While effective, the decoding procedure induce
considerable computational overhead, rendering the conversational model
unsuitable for interactive usage. To overcome this, we introduced an approach
that does not require further computation at decoding time, nor does it
require any fine-tuning of a large language model. We demonstrated, through
extensive automatic and human evaluation, a high degree of control over the
generated conversational responses with regard to multiple desired attributes,
while also being fluent.

In continuously controlling and extending task-oriented dialogue systems, we
proposed a benchmark for task-oriented dialogue systems with 37 domains to be
learned continuously in four settings: intent recognition, state tracking,
natural language generation, and end-to-end. Moreover, we implemented and
compared multiple existing continual learning baselines, and we proposed a
highly controllable architectural method based on residual adapters. Our
experiments demonstrated that the proposed architectural method and a simple
replay-based strategy perform comparably well, but they both achieve inferior
performance to the multi-task learning baseline, in which all the data are
shown at once, showing that this setting is a challenge. Furthermore, we
reveal several trade-offs between learning methods in terms of parameter usage
and memory size, which are important in the design of task-oriented dialogue
systems.

In learning to compose and control multi-skill dialogue models, we proposed to
learn a dialogue system that independently parameterizes dialogue skills, and
learns to select and combine each of them through Attention over Parameters
(AoP). The experimental results showed that this approach achieves competitive
performance on a combined dataset of MultiWOZ, In-Car Assistant, and Persona-
Chat, and we demonstrated that each dialogue skill is effectively learned and
can be combined with other skills to produce selective responses.

Overall, the common technique proposed in the thesis for controlling
style&topics, continuous domain learning and multi-skills system, is the
independent parameterization of different dialogue skills. This is done by
using residual adapters in style&topics and continuously learning domains, and
by using Transformer decoders in multi-skill systems. Independently by the
network choice, the parametrization of each dialogue skill leads to highly
controllable, extendable and composable dialogue systems. The choice of
residual adapter is especially meaningful for lightweight plug-and-play skills
like style&topics and adding dialogue skills through time. While mixing
different Transformer decoders is important in multi-skills systems since
allows to mix and match different skills and achieve compositional behaviours.

In future work, we expect to develop dialogue systems that are easy to control
and are also more data-efficient and can learn from user interaction rather
than supervised data. In fact, in the three chapters of this thesis, we open
new exciting research directions for dialogue systems: 1) controlling style
and topic is still in its infancy. In Chapter 3, we control seven attributes,
while in future works, we would like to extend our methodology to control many
more attributes (e.g., emotions) and to compose multiple adapters to achieve
compositionality (e.g., positive + sport etc.). 2) controlling how to extend
dialogue systems with a clear distinction between tasks is challenging and
exciting, as seen in Chapter 4. However, removing the distinction between
tasks leads to a more challenging setting where the model is forced to learn
from a data sample stream. This streaming learning reassembles human learning
and would make a model that can learn from user interaction rather than
supervised data. 3) controlling multi-skills dialogue systems opens future
research directions similar to point 1 and 2, plus the possibility of studying
more advanced methods to compose different skills to obtain exponential
dialogue skills by using only a linear number of experts. Although in Chapter
5, we show some compositional behaviours, it is still an open and exciting
challenge to achieve compositional behaviours with more dialogue skills (e.g.,
style in task-oriented dialogue).

Figure 6.1: The beasts are tamed.

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# Estimating Leaf Water Content using Remotely Sensed Hyperspectral Data

Vishal Vinod
Indian Institute of Science Bangalore
<EMAIL_ADDRESS>Rahul Raj, Rohit Pingale, Adinarayana Jagarlapudi
Indian Institute of Technology, Bombay
<EMAIL_ADDRESS>
##  1 Introduction

Plant water stress may occur due to the limited availability of water to the
roots/soil or due to increased transpiration. These factors adversely affect
plant physiology and photosynthetic ability to the extent that it has been
shown to have inhibitory effects in both growth and yield [18]. Early
identification of plant water stress status enables suitable corrective
measures to be applied to obtain the expected crop yield. Further, improving
crop yield through precision agriculture methods is a key component of climate
policy and the UN sustainable development goals [1]. Leaf water content (LWC)
is a measure that can be used to estimate water content and identify stressed
plants. LWC during the early crop growth stages is an important indicator of
plant productivity and yield. The effect of water stress can be instantaneous
[15], affecting gaseous exchange or long-term, significantly reducing yield
[22, 9, 18]. It is thus necessary to identify potential plant water stress
during the early stages of growth [15] to introduce corrective irrigation and
alleviate stress. LWC is also useful for identifying plant genotypes that are
tolerant to water stress and salinity by measuring the stability of LWC even
under artificially induced water stress [18, 25]. Such experiments generally
employ destructive procedures to obtain the LWC, which is time-consuming and
labor intensive. Accordingly, this research has developed a non-destructive
methodology to estimate LWC from UAV-based hyperspectral data.

##  2 Related Work

Previous works have explored water sensitive wavelengths [11, 17] and indices
[13] for estimating LWC. However, most of the work is done with 400-2500 nm
data. These sensors are expensive and arduous to maintain. [14] discuss the
differences between greenhouse and real-world outdoor monitoring with UAVs,
and show drought prediction from spectral band data and general vegetative
indices (NDVI, PRI, etc.). Recent works [10, 29] present methods for
classification using Hyperspectral images (HSI). [16] shows that the spatial
variability of chlorophyll from HSI may correlate with LWC. Several works [28,
30, 32] describe methods to estimate water and nitrogen levels in the crop
using non-destructive means. However, very few of them have used cross-sensor
data modeling. Accordingly, this work investigates water-sensitive indices
from hand-held spectroradiometer data that affect water content and train
ensemble regressors to compute LWC. A threshold of $79\%$ [15] is used to
classify the water stress status of each pixel in the UAV-captured
hyperspectral image based on the LWC estimation for the six-leaf growth stage.
Further, this work also presents qualitative results for temporal variation of
LWC during various stages of crop growth for water stress forecasting. This is
the first work to estimate LWC by identifying pure-pixel water-sensitive
indices in the 400-1000nm range of the spectroradiometer data to train
ensemble methods that generalize for UAV-captured hyperspectral images in
real-world outdoor environments.

##  3 Methodology

Figure 1: Pixel-wise Crop Water Stress Prediction for 20 Nov 2018 (DAS=36)
data. (a) Aerial image of plot, (b) Super-pixel $1m\times 1m$ plot computed
using Raj _et al_. [20] method used as pseudo ground-truth for LWC estimation,
(c) The LWC estimation plot with an NDVI threshold of $0.7$ for the proposed
method using GBM, (d) Water stress classification using 79% threshold.

Dataset. The experimental farmland consists of 27 specialized treatment plots
with varying levels of fertilizer and water provided at regular intervals. The
data collected includes hyperspectral data recorded from a hand-held
spectroradiometer and a UAV-based Hyperspectral camera to capture HSI of the
entire farmland at various stages of growth. The spectroradiometer data
consists of 483 data points each with 382 Hyperspectral band data, DAS (Days
After Sowing), Nitrogen, Carbon, CNRatio and LWC. The LWC ground-truth value
is obtained from destructive distillation of the leaves from the respective
plots.

Water-sensitive Indices. Water absorption wavelengths in the 400-1000nm range
are more practical to collect due to minimal overhead in maintenance and cost
of the sensors. Previous works [21, 27] show that there is a secondary set of
water-sensitive wavelengths in the 400-1000nm range. Thus, this work uses the
s range for data collection and conduct 400-1000nm range correlation analysis
to identify water-sensitive indices. The indices used are tabulated in Table
1. NDVI has been shown to correlate with water stress [5, 4, 8] and is also a
common indicator of crop health [2]. The differences in water treatment in the
27 specialized treatment plots may lead to variations in foliage color, and
under these conditions the green and red normalized indices [3] are more
water-sensitive than NDVI [4, 12], hence the Green NDVI and Red Edge [31]
indices have been used in this work. There exist secondary water-sensitive
bands at 950-970nm [3, 26, 8] and several works [21, 12, 27, 3] show the
direct decrease in Water Index (WI) with water stress. The Renormalized DVI
indices (RDVI) [24] and Optimized Soil Adjusted Vegetation Index (OSAVI) [23,
19] have shown to be good predictors of LWC. Further, rigorous analytic
experiments show a strong correlation (coefficient: 0.84) among MTVI2, OSAVI
and RDVI. This work builds on the findings in [20] to identify NIR1, RedBlue
and WaterBand indices to have a strong correlation with LWC.

Table 1: The indices used to estimate leaf water content.

Indices | Formula
---|---
NDVI | $(\rho_{\textrm{nir}}-\rho_{\textrm{red}})/(\rho_{\textrm{nir}}+\rho_{\textrm{red}})$
Green NDVI | $(\rho_{\textrm{nir}}-\rho_{\textrm{green}})/(\rho_{\textrm{nir}}+\rho_{\textrm{green}})$
RDVI | $(\rho_{\textrm{nir}}-\rho_{\textrm{red}})/(\rho_{\textrm{nir}}+\rho_{\textrm{red}})^{1/2}$
MTVI2 | $\frac{1.5\times(1.2\times(\rho_{\textrm{nir}}-\rho_{\textrm{green}})-2.5\times(\rho_{red}-\rho_{green})]}{[(2\times\rho_{\textrm{nir}}+1)^{2}-(6\times\rho_{\textrm{nir}}-5\times\rho_{red}^{1/2})-0.5]^{1/2}}$
Water Index | $\rho_{\textrm{900}}/\rho_{\textrm{970}}$
NPCI | $(\rho_{\textrm{680}}-\rho_{\textrm{430}})/(\rho_{\textrm{680}}+\rho_{\textrm{430}})$
OSAVI | $[1.16*(\rho_{\textrm{800}}-\rho_{\textrm{670}})]/(\rho_{\textrm{800}}+\rho_{\textrm{670}}+0.16)$
Red Edge | $\rho_{\textrm{750}}/\rho_{\textrm{710}}$
NIR1 | $(\rho_{\textrm{800}}-\rho_{\textrm{847}})/(\rho_{\textrm{800}}+\rho_{\textrm{847}})$
RedBlue | $(\rho_{\textrm{660}}-\rho_{\textrm{420}})/(\rho_{\textrm{660}}+\rho_{\textrm{420}})$
Water Band | $(\rho_{\textrm{791}}-\rho_{\textrm{970}})/(\rho_{\textrm{791}}+\rho_{\textrm{970}})$

Estimating Leaf Water Content. Ensemble methods and regressors were trained on
the handheld spectroradiometer data (see Table 2). Note that the experiment
with three indices corresponds to using NIR1, RedBlue and WaterBand indices,
and eight indices correspond to the remaining indices listed in Table 1. The
Gradient Boosting [6, 7] method demonstrated the best RMSE and $R^{2}$ metrics
for the eleven indices experiment (see Table 2). The trained Gradient Boost
method is used for the inference stage on the geo-tagged UAV-captured high-
resolution hyperspectral images (HSI). The performance is compared with the
super-pixel 1m x 1m plot computed by Raj _et al_. [20] as psuedo ground truth
for the surrounding pixels. As discussed in Sec.(2), the threshold of $79\%$
water content during the six-leaf stage DAS=36 (DAS: Days After Sowing) of the
crop to classify the water stress status. Fig.(1) shows LWC inference and
stress prediction on the UAV-captured hyperspectral images for DAS=36 compared
with the $1m\times 1m$ super-pixel plot.

Regression | 8 Indices | 3 Indices | 11 Indices
---|---|---|---
Algorithm | RMSE | $R^{2}$ | RMSE | $R^{2}$ | RMSE | $R^{2}$
Gradient Boost | 0.0344 | 0.641 | 0.0158 | 0.925 | 0.0153 | 0.930
Lasso Cross-Val | 0.0361 | 0.610 | 0.0172 | 0.911 | 0.0171 | 0.912
Random Forest | 0.0354 | 0.622 | 0.0165 | 0.918 | 0.0161 | 0.922
Stacked Methods | 0.0338 | 0.658 | 0.0162 | 0.921 | 0.0154 | 0.929

Table 2: Comparing LWC estimation prediction with different subset of indices.
We report the corresponding RMSE and $R^{2}$ metrics.

##  4 Conclusion

This work identifies indices that are correlated with leaf water content and
water stress. First, the values of the indices from the spectral bands of the
hand-held spectroradiometer data are computed. Then, ensemble methods are
trained on this dataset with high-quality LWC ground truth values and the
$1m\times 1m$ super-pixel pseudo ground truth verifying the improvement in
RMSE and $R^{2}$ metric using the proposed set of eleven indices. The trained
model is evaluated on UAV-captured hyperspectral images for varying stages of
growth and DAS to compute pixel-level LWC statistics for each treatment plot.
The comparison between the mean predictions on the HSI with the psuedo ground
truth mean water content values for each plot verify the performance of the
method in estimating LWC effectively.

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