# DESYR: Definition and Syntactic Representation Based Claim Detection on the
Web

Megha Sundriyal1*, Parantak Singh2*, Md Shad Akhtar1
Shubhashis Sengupta3, Tanmoy Chakraborty1 1IIIT-Delhi, India. 2BITS Pilani,
Goa, India. 3Accenture Labs Bangalore, India. India meghas, shad.akhtar,
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
(2021)

###### Abstract.

The formulation of a claim rests at the core of argument mining. To demarcate
between a claim and a non-claim is arduous for both humans and machines, owing
to latent linguistic variance between the two and the inadequacy of extensive
definition-based formalization. Furthermore, the increase in the usage of
online social media has resulted in an explosion of unsolicited information on
the web presented as informal text. To account for the aforementioned, in this
paper, we propose DESYR. It is a framework that intends on annulling the said
issues for informal web-based text by leveraging a combination of hierarchical
representation learning (dependency-inspired Poincaré embedding), definition-
based alignment, and feature projection. We do away with fine-tuning compute-
heavy language models in favor of fabricating a more domain-centric but
lighter approach. Experimental results indicate that DESYR builds upon the
state-of-the-art system across four benchmark claim datasets, most of which
were constructed with informal texts. We see an increase of 3 claim-F1 points
on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1
points on the Online Comments (OC) dataset, an increase of 24 claim-F1 points
and 17 macro-F1 points on the Web Discourse (WD) dataset, and an increase of 8
claim-F1 points and 5 macro-F1 points on the Micro Texts (MT) dataset. We also
perform an extensive analysis of the results. We make a 100-D pre-trained
version of our Poincaré-variant along with the source code.

Claim Detection, Poincaré Embedding, Feature projection, Informal texts,
Linguistic Grounding, Twitter, Social media

* First two authors have equal contributions. The work was done when Parantak was an intern at LCS2 Lab, IIIT-Delhi.

††journalyear: 2021††copyright: acmcopyright††conference: Proceedings of the
30th ACM International Conference on Information and Knowledge Management;
November 1–5, 2021; Virtual Event, QLD, Australia††booktitle: Proceedings of
the 30th ACM International Conference on Information and Knowledge Management
(CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia††price:
15.00††doi: 10.1145/3459637.3482423††isbn: 978-1-4503-8446-9/21/11

ACM Reference Format:
Megha Sundriyal, Parantak Singh, Md Shad Akhtar, Shubhashis Sengupta, Tanmoy
Chakraborty. 2021. DESYR: Definition and Syntactic Represen-tation Based Claim
Detection on the Web. In Proceedings of the 30th ACM International Conference
on Information and Knowledge Management (CIKM’21), November 1-5, 2021, Virtual
Event, QLD, Australia. ACM, New York, NY, USA, 10 pages.
https://doi.org/10.1145/3459637.3482423

##  1\. Introduction

The increasing vogue behind Online Social Media (OSM) has led to a colossal
sway within the media-consuming audience. Participation over OSM has swiveled
into another corresponding and associate phenomenon. We are incessantly
exposed to news, media, opinions, and perspectives. Such a constant barrage of
information can, in turn, increase the plausibility behind resources being
mishandled or exploited.

The 45th Presidential US elections witnessed the alarming impact of fake news
where a substantial percentage of their citizens were influenced by a
malicious website (Grave et al., 2018). In their study, Allcott and Gentzkow
(2017) revealed that every American clicked on at least one fake news article
related to the presidential candidates during the elections. The recent cases
of spreading fear and prompting false cures due to fake news spread have
threatened countless lives during the COVID-19 global pandemic (News, 2020).
The effect of claims applies unprecedentedly and often leads to the loss of
money and precious human life. Similarly, several such cases have been popping
up within the global community. Several machine learning and natural language
processing models have been proposed to handle fake news and the automated
detection of erroneous claims. Within recent literature, fake news detection
has been embellished into more of an umbrella term that encapsulates sub-tasks
like stance detection, hostility detection, claim detection, etc.

Argument Mining (AM) has been significantly confronted by NLP for the past few
decades. Segmenting argumentative and non-argumentative texts, detecting
claims, and parsing argument assemblies are some of the main concentrations
within this field. Narrowing down from AM into the domain of claim detection,
the task in layman terms is to detect sentences containing a claim. As an
elementary intuition, claim detection is a pre-cursor to fact-checking,
wherein segregation of claims aids in restricting the corpus that needs a
fact-check.

Toulmin (2003) initiated the early works on argumentation in the 1950s; in his
argument conjecture, he described a claim as ‘an assertion that needs to be
proven’. Formally, according to the Oxford Dictionaries , a claim is defined
as: ”to say that something is true although it has not been proven and other
people may not believe it” 111Claim - Oxford Definition. The concept of false
claims is entrenching its roots in every domain: online social media
platforms, news articles, political agendas, and so forth. The divergence
boundary between a claim and a non-claim is very thin, making the conception
of a claim very subjective and abstruse. As a consequence, the categorization
of claims is taxing for both human annotators and state-of-the-art neural
models alike. Herein, the difficulty exists given the disparity in perception
and the lack of an existing formalization for claims. The more classical works
in claim detection practiced syntactic composition, where they cashed in on
the use of combinatorial distillation from context-free grammars, constituency
parse-trees, and other linguistic renditions to design their models (Levy et
al., 2014; Lippi and Torroni, 2015). The more recent works shifted towards
neural approaches, and leveraging large Language Models (LM) in an attempt at
capturing dormant features through explicit linguistic encapsulation
(Daxenberger et al., 2017; Chakrabarty et al., 2019). In our previous work
(Gupta et al., 2021), we attempted to encode both linguistic and contextual
knowledge to study claims across different distributions. The literature
across the encompassing of OSM-based informal texts and the fusion of
structure and context for claim detection is still sporadic and needs to be
driven. Table 1 shows a few representative examples of claims and non-claims.

Table 1. Representative examples of claims and non-claims. Explicit expressions of claims are in italics. Text | Label
---|---
@realDonaldTrump A lot of people are saying cocaine cures COVID-19. So. | Claim
Injecting disinfectant might not cure #coronavirus, but TheLastDance sure is saving lives during #Lockdown. | Claim
maybe if i develop feelings for covid-19 it will leave | Non-claim
Olympic events are rooted in old traditions. | Non-claim

#### Motivation:

Claims, as is very apparent, subsist across a variety of distributions, from
essays, to Wikipedia articles, to OSM posts and comments, etc. However, with
the voluminous explosion of data on social media, it is of paramount
importance that we concentrate distinctively upon claims distributed through
OSM (Baum et al., 2021; Organization et al., 2020). The web has become the
pivot for all things social and global, and a vast majority of this human
expression comes in textual form, specifically short, informal texts (Twitter,
Facebook, etc.). More often than not the conformity bias (Whalen and Laland,
2015) comes into play and pits users of strong wide-ranged opinions against
each other, and can at times end up promoting something erroneous. For
example, a tweet that reads ‘drinking an antiseptic cures COVID’ can be a
cause for massive unrest and demands an immediate fact check. Visibly, with
the increasing magnitude of data, automated furthering of texts that comprise
a claim into a fact-verification pipeline should garner gravity.

Another qualm, as previously mentioned, is the issue of division between
claims and non-claims. These, at times, tend to lie in a domain of high
invariability. Additionally, the severe imbalance of existing datasets leads
to data severance (for performance augmentation) within already small
datasets. On top of this, existing systems have inclined towards large LMs and
provided a profusion of promise. However, they are, without a doubt,
computationally expensive. We address the following intricacies and propose a
framework that pursues the objective of erecting discernible feature spaces
for the individual classes while avoiding the use of LMs and fixating on a
linguistic and definition centring approach.

#### Proposed Methodology:

We propose DESYR, a DEfinition and SYntactic Representation based claim
detection model that achieves competence over the better segregation of
feature space for classification, and moreover, learns to leverage the
guidelines for identifying claims and non-claims proposed in our previous work
(Gupta et al., 2021) for furtherance of the feature constitution. It attains
this by employing an intelligible unity of feature projection, attention-based
alignment, and pre-transformer Deep Learning. Also, in line with Lippi and
Torroni (2015) who argued that a claim could be grounded in linguistics, we
propose DARÉ, the Dependency-PoincARÉ embedding, which is a dependency-
inspired variant of Poincaré embedding (Nickel and Kiela, 2017). It helps
assimilate enhanced representations of word vectors by capturing intrinsic
hierarchies in dependency trees.

We evaluate DESYR across four web-based datasets which comprise short informal
texts and observe up to par results throughout. We conduct a result analysis
across several baselines, along with a general analysis for our predictions.
The comparative investigation espouses the finer performance of DESYR compared
against other state-of-the-art systems.

#### Summary of the major contributions:

  * •

DESYR, a claim detection system tailored towards informal web-based texts. The
said system determines the existence of claims in online text by aligning the
query to encoded definitions and projecting them into a purer space.

  * •

Novel combination of gradient reversal layer and attentive orthogonal
projection. To the best of our knowledge, the usage of gradient-reversal layer
to learn the label-invariant features and leveraging an attentive orthogonal
projection for drawing the choicest feature representations in the domain of
claim detection is the primary attempt.

  * •

Comprehensive evaluation and state-of-the-art results. We evaluate DESYR
against the state-of-the-art systems and LM baselines across four benchmark
datasets. The contrast suggests the superiority of our architecture, and our
ablation study highlights the importance of each module.

  * •

DARÉ, our novel dependency-inspired Poincaré variant. It shows promise for
linguistically-grounded NLP tasks. We release a pre-trained 100-dimensional
version of DARÉ.

#### Reproducibility:

We release the 100-D pre-trained version of our Poincaré-variant (DARÉ) and
source code of DESYR for further research at
https://github.com/LCS2-IIITD/DESYR-CIKM-2021.

Figure 1. A schematic diagram of the DESYR framework for the claim detection.
The spotlight network, s-net, is the backbone of DESYR that incorporates the
alignment of input text considering the claim and non-claim definitions.
Moreover, it selectively extracts the relevant features through an attentive
orthogonal projection of label-invariant features (from r-net) and the aligned
input representation (from d-net). The auxiliary classification layers help
DESYR in learning sub-modules directly from the gradients’ signals.

##  2\. Related Work

Claims are an indispensable component of AM, and similarly claim detection is
equally as crucial for the fake news detection pipeline. With the expansion of
OSM, misinformation proliferated through claims now undeniably poses a greater
risk to consumers. With zero deterrence, the spread of misinformation can be
rapid and harmful. Therefore, claim detection has recently gained traction as
an NLP task which works a precursor to automated fact verification.

In 2011, Bender et al. (2011) proposed an annotation system and presented the
Authority and Alignment in Wikipedia Discussions (AAWD)
corpus222http://ssli.ee.washington.edu/projects/SCIL.html which consisted of a
collection of around 365 discussions curated from Wikipedia Talk Pages (WTP).
Their work grabbed a lot of attention from researchers on claims and provided
a basis for this challenging task. In the past decade, the study of claim
detection procured some drag within the NLP research community with a
principal attempt by Rosenthal and McKeown (2012). They endeavoured on mining
claims from discussion platforms and implemented a supervised approach based
on sentiment and word-gram derivatives. Although their work was restricted to
a classical machine learning approaches, it formed the basis for future works
in this field.

Levy et al. (2014) put forward a context-dependent claim detection (CDCD)
model. They described a ‘context-dependent claim’ as ”a general, concise
statement that directly supports or contests the given topic”. They exercised
their approach only over Wikipedia scripts while maneuvering context-based and
context-free feature sets for spotting claims. Lippi and Torroni (2015)
propounded the context-independent claim detection (CICD) model that employed
constituency parse trees to capture structural knowledge without an explicit
encapsulation of context. The pitfall being that they only engineered this
around a thoroughly superintended Wikipedia corpus.

Levy et al. (2017) proposed the first unsupervised approach for claim
detection. According to the authors, a claim begins with the word ‘that’ and
the main concept (MC) or a topic name then follows. This work, however, is
restrictive to distributions which are accompanied by formal texts. Most
literature on claim focuses focuses on the domain specificity. As a result,
there is a lack of generalization in existing systems. To confront this,
Daxenberger et al. (2017) performed a qualitative analysis across six datasets
and argued that the anatomy of claims stands differently within different
distributions.

In the more recent times, the study of claims too, trended towards the
utilization of transformers. Chakrabarty et al. (2019) used over 5 million
self-labeled Reddit comments that contained the abbreviations IMO (In My
Opinion) or IMHO (In My Honest Opinion) to fine-tune their LM expecting to
gravitate their distribution towards their conceptualization of a claim. They
however, made no evident attempt at encapsulating the syntactic properties.
Gupta et al. (2021) leveraged both semantic and latent syntactic features
through an amalgamation of linguistic encoders (part-of-speech and dependency
based) and a contextual encoder (BERT). They additionally annotated a Twitter
dataset and proposed thorough guidelines that were centred around annotating
claims. Cheema et al. (2021) investigated the role of images on claims. They
presented a novel framework on leveraging dual modalities for claim detection.

The CLEF-2020 shared task (Barrón-Cedeno et al., 2020) witness several models
that were specifically tweaked for claim detection. Williams et al. (2020)
bagged the cake with the fine-tuned RoBERTa (Liu et al., 2019) model that was
accentuated by mean pooling and dropout. Nikolov et al. (2020) took the second
place with their out-of-the-box RoBERTa vectors that were heightened by
Twitter metadata.

DESYR attempts at abrogating the drawbacks from previous works - we propose an
architecture grounded in linguistics and expedited by context and definition-
based alignment.

##  3\. Proposed Methodology: DESYR

The more conventional renderings of claim detection are constructed with
either contextual methodologies (Chakrabarty et al., 2019; Daxenberger et al.,
2017) or syntactic methodologies (Levy et al., 2017; Lippi and Torroni, 2015)
at the centre. Recently, in our precedent work (Gupta et al., 2021), we
proposed the LESA architecture that leverages individually-furnished
contextual and linguistic encoders combined into one for improved performance
on claim detection. Moreover, in the helm of natural language processing, we
observe increasing use of language models and fine-tuning in addition to other
modules in the architecture – a computationally heavy process with a
significant number of learnable parameters. In LESA (Gupta et al., 2021), we
incorporated three modules including two transformer-based models for the
claim detection, thus making the architecture heavy on compute. Additionally,
within the literature, we observe an inferior performance on the minority
class, despite efforts on imbalance eradication. Given our outlook on the
assimilation of syntax and context and driven by an attempt to prune the
aforementioned drawbacks, we now propose DESYR.

DESYR leverages representation learning by exercising a novel dependency-
inspired variant of the Poincaré embedding (Nickel and Kiela, 2017).
Furthermore, we aim at amputating the class-invariant features, and obtaining
superior class-representing features by incorporating the technique for the
feature projection, highlighted by Qin et al. (2020). DESYR’s backbone
comprises of two networks in parallel – the regulation-net (r-net) and the
spotlight-net (s-net). The regulation-net learns the class-invariant features
through a gradient-reversal layer (Ganin et al., 2016). On the other hand, the
spotlight-net draws representations of the input which are devoid of the
class-invariant features, thereby allowing for better distinction between our
binary labels. Both r-net and s-net incorporate a feature extractor module,
called feature-net (f-net), in their respective modules – f-nets and f-netr.
In addition, we also incorporate a definition network (d-net) that aims at
aligning input texts to definitions of a claim and non-claim to further
augment class variance. We leverage d-net in our spotlight network module by
enabling the feature network (f-nets) to learn the segregation between claim
and non-claim definitions. We hypothesize that learning such alignment would
be exploited by DESYR in successive layers for the claim detection. In
particular, we fine-tune the hidden representation in s-net to extract the
essence of segregation between the claim and non-claim definition through
d-net.

Moreover, DESYR is specifically calibrated towards short informal web-based
texts, and we leverage claim and non-claim definitions proposed in (Gupta et
al., 2021) to engineer our d-net. Intricate details of the aforementioned
modules are discussed in further sections. We present a high-level
architecture for DESYR framework in Figure 1.

###  3.1. Dependency-Poincaré Embedding (DARÉ)

The Depedency-poincARÉ (DARÉ) embedding is DESYR’s variant of the Poincaré
embedding Nickel and Kiela (2017). Employing DARÉ, we attempt to capture
latent linguistic properties in textual dependency. Below, we briefly
introduce dependency parsing followed by a dossier on how the former was
imbued into the Poincaré ball.

Dependency parsing is a function that maps a sequence of tokens
{$n_{1},n_{2},...$} to a dependency tree. A dependency parse tree is a
directed graph with $N$ nodes and $E$ edges, where each node represents an
individual token $n_{i}$, and each edge represents the syntactic dependency
between $n_{i}$ and $n_{j}$. An edge
$n_{i}\overset{d_{k}}{\rightarrow}{n_{j}}$ comprises a parent node directed at
the child node with the dependency $d_{k}$, where $d_{k}$ is the nature of the
dependency. We employ spaCy333https://spacy.io/ for the dependency parsing. An
example is shown in Figure 2.

Figure 2. Hierarchy (graph) formulation for DARÉ training: dependency-based
tree for the sentence ‘A hearing is scheduled on the issue today’.

#### Poincaré Embedding:

As highlighted by Nickel and Kiela (2017), embedding hierarchical graph-based
information in Euclidean space can be difficult, owing to the exponential
growth of nodes which brings leaf nodes in different branches close to one
another thereby distorting hierarchies. With the Poincaré ball, the extent
from the center grows exponentially, allowing one to fit an erratic amount of
levels in the hyperbolic space. We then optimize our word vectors on this
space and optimize the following loss function with negative-sampling:

(1) $L(\theta)=\sum_{(x,y)\in\
\Delta}\log\frac{\exp^{-dist(x,y)}}{\sum_{y^{\prime}\in\
R(x)}\exp^{-dist(x,y^{\prime})}}$

where $dist(x,y)$ is the Poincaré distance such that,

(2)
$dist(x,y)=\text{arccosh}(1+2\frac{||x-y||^{2}}{(1-||x||^{2})(1-||y||^{2})})$

where $\theta$ is our set of vectors, $\Delta$ is the set of all embedded
hierarchies-cum-dependencies (in this case, $\forall\ n_{i}\rightarrow
n_{j}$), and $R(x)$ is the set of random tokens that are not associated with
$x$. Additionally, we attempt at the disambiguation of part-of-speech (POS) by
formulating our $\theta$ as word vectors of the tokens augmented with their
POS, such that $x=n_{i}.\text{POS}_{i}$. Our loss function $L$ is trained
similar to how it would be in Euclidean space. However, the only difference is
that we employ Riemann Gradient Descent (Bonnabel, 2013) for the optimization.
We utilize Gensim’s
implementation444https://radimrehurek.com/gensim/models/poincare.html to train
DARÉ.

###  3.2. Feature Net (f-net)

Prior to discussing r-net and s-net, we discuss the component f-net which is
common to both the aforementioned networks and serves as the feature
extractor. To put this into context, an f-net comprises of stacked BiLSTMs,
whose hidden units are then processed by a sequential self-attention
mechanism, as suggested by Zheng et al. (2018).

Inspired by the Inception (Szegedy et al., 2015) architecture, we optimize
f-net through an auxiliary softmax layer. The intuition behind having these
auxiliary outputs is that they would act as implicit assistance against the
vanishing gradient problem and make low-level features of the network more
accurate. As mentioned before, the transitional outputs (all hidden units)
from the BiLSTM are used as inputs for our r-net and s-net. To emphasize
again, each has an individual f-net with no shared parameters.

###  3.3. Definition Net (d-net)

As mentioned earlier, the only distinguishing feature between f-nets and
f-netr is that the former comprises of d-net. The d-net module helps in
aligning the inputs to predefined guidelines/definitions that elucidate the
characteristics of claims and non-claims. Intuitively, for any given input
text, we find its alignment against the aforementioned sets of definitions
using an attention-based mechanism. This helps us draw divergent associations
from the input with respect to claims and non-claims.

To put formally, suppose we have two sets of definitions,
$C=[c_{1},c_{2},\cdots,c_{n}]$ for claims and
$NC=[nc_{1},nc_{2},\cdots,nc_{m}]$ for non-claims. The input text $t$ forms
our query; this query is then processed (discussed below) against each of the
definitions in $C$ and $NC$ to get the claim-definition-map and the non-claim-
definition-map, respectively.

The d-net comprises of d-netC and d-netNC. These are carbon-copies where one
is initialized with the definitions of claims and other with those of non-
claims. We process these definitions and input text through a BiLSTM encoder.
For each definition, we obtain a value vector, and for the input text, we take
the last time-step representation as the query vector. Subsequently, for each
definition encoding (valuei), we calculate its attention score (Luong et al.,
2015) against the query. Finally, the query-value-attention-score is pooled
(global average) over the sequence axis to obtain a 1-dimensional
representation. We repeat the process for each pair of query and definition –
$\langle$query, $c_{i}\rangle\forall i=\\{1,2,\cdots,n\\}$ and $\langle$query,
$nc_{j}\rangle\forall j=\\{1,2,\cdots,m\\}$. The concatenation of all query-
value-attention-scores forms the definition-based representation for $t$
against the respective definition-set.

We append the representations from d-netC and d-netNC behind the BiLSTM output
at each time step to enhance the feature learning in f-net${}_{\textit{s}}$
and thereby, explicitly helping the transient BiLSTM features to become more
archetypal. The d-net is only part of f-net${}_{\textit{s}}$ owing to the fact
that s-net is our primary classifier whereas r-net acts analogously to a
feature-selector.

###  3.4. Regulation Net (r-net)

The regulation network acts to collect class-invariant (common, shared amongst
the classes) vector representations from f-net${}_{\textit{r}}$. It functions
as a network trained in parallel to the s-net. As highlighted in (Qin et al.,
2020; Ganin and Lempitsky, 2015), we employ a Gradient Reversal Layer (GRL) to
capture the class-invariant features. In a nutshell, a gradient-reversal layer
can be thought of as a pseudo-functional mapping where the forward and
backward propagation are respectively defined by two opposed equations as
follows:

(3) $\textit{GRL}(x)=x\qquad\frac{\delta\textit{GRL}(x)}{\delta x}=-I$

The transient BiLSTM’s output from f-net${}_{\textit{r}}$, that serves as the
input to r-net, learns the invariant features and is then drifted to s-net for
computing the orthogonal projection.

Table 2. Statistics of four datasets used in our experiments.

Dataset | Twitter | Online Comments (OC) | Web Discourse (WD) | Micro Text (MT)
---|---|---|---|---
| Claim | Non-claim | Claim | Non-claim | Claim | Non-claim | Claim | Non-claim
Train set | 7354 | 1055 | 623 | 7387 | 190 | 3332 | 100 | 301
Test set | 1296 | 189 | 64 | 730 | 14 | 221 | 12 | 36
Overall | 8650 | 1244 | 687 | 8117 | 204 | 3553 | 112 | 337

###  3.5. Spotlight Net (s-net)

The spotlight network is the prime module of DESYR. We amalgamate the class-
invariant features of r-nets through an attention orthogonal project layer
(a-OPL). The orthogonal projection layer aims at drawing the choicest feature
representations (Qin et al., 2020).

For convenience, we refer to the feature representation from
f-net${}_{\textit{s}}$ and f-net${}_{\textit{r}}$ as
$\textit{v}_{s}\in\mathbb{R}^{l\times d}$ and
$\textit{v}_{r}\in\mathbb{R}^{l\times d}$, respectively.

We design s-net to extract the semantic representation for our input text $t$
and project the same into a non-homogeneous domain space. To accomplish this,
we project $v_{s}$ onto the orthogonal direction of $v_{r}$. The space
orthogonal to $v_{r}$ should, in theory, be rid of class-homogeneity, and
projecting $v_{s}$ onto it should lead to discriminative information being
stripped of class-invariant knowledge. Further, we describe the mathematical
details of a-OPL. The projection between two vectors $u$ and $v$ is defined
as,

(4) $proj(u,v)=\frac{u.v}{|v|}\frac{v}{|v|}$

Utilizing the above equation, we project $v_{s}$ onto $v_{r}$ while tending to
each time-step from their respective LSTM using a TimeDistributed Layer (TDL)
such that,

$\begin{split}v_{(s,i),(r,i)}&=\textit{proj}(v_{(s,i)},v_{(r,i)})\\\
v_{s,r}&=\textit{TDL}_{\forall i}(v_{(s,i),(r,i)})\end{split}$

We then find the projection of $v_{s}$ in the orthogonal direction of
$v_{s,r}$ while again tending to each step using a TDL such that,

$\begin{split}\widetilde{v}_{(s,i)}&=\textit{proj}(v_{(s,i)},(v_{(s,i)}-v_{(s,i),(r,i)}))\\\
\widetilde{v}_{s}&=\textit{TDL}_{\forall i}(\widetilde{v}_{(s,i)})\end{split}$

To further refine this feature representation, we then attend to each time-
step in $\widetilde{v_{s}}$ using sequential self-attention (Zheng et al.,
2018) such that,

(5)
$attentive_{\widetilde{v}_{s}}=\widetilde{v_{s}}^{a}=softmax(\sigma(W_{a}\widetilde{v}_{s,i}+b_{a}))$

The aforementioned forms the basis for our a-OPL. The attentive vector
$\widetilde{v_{s}}^{a}$ is then utilized for classification.

We train s-net and r-net parallel to each other and employ sparse categorical
focal loss. In a classification setting, with labels $y$, the loss is defined
as,

(6) $L(y,\hat{p})=-(1-\hat{p}_{y})^{\gamma}log(\hat{p}_{y})$

where $p$ is a vector that represents the approximate probability distribution
across our two classes, and $\gamma$ is the focusing parameter, which in
essence acts to down-weigh easy-to-classify examples. Higher $\gamma$ implies
high discounting of the easy-to-classify examples.

##  4\. Datasets

Owing to our formulation of a claim detection system for informal texts on the
web, we accumulate four publicly-available web-based datasets as mentioned
below:

  * •

Twitter Dataset: It is a claim detection dataset of COVID-19 tweets released
recently (Gupta et al., 2021). The dataset consists of $\sim{10k}$ labeled
tweets. The authors also released a set of definitions for claim and non-claim
text in their annotation guidelines555https://bit.ly/3yAhhW0. As mentioned
earlier, we utilize them for our definition network (d-net). In total, the
annotation guidelines consists of 10 claim definitions and 8 non-claim
definitions.

  * •

Online Comments (OC): Biran and Rambow (2011) released a dataset containing
comment threads from the blog posts on LiveJournal.

  * •

Web Discourse (WD): Habernal and Gurevych (2015) released a dataset containing
blog posts and user comments stripped to individual sentences and/or phrases.

  * •

Micro Text (MT): Peldszus and Stede (2015) released a corpus of $\sim$500
discussions on controversial issues.

Data statistics coupled to the four assimilated datasets are provided in Table
2.

We can observe that, as opposed to the Twitter dataset, MT, OC and WD datasets
are imbalanced towards non-claims. Due to the label skewness, we adopt a
sampling mechanism, similar to our precedent work (Gupta et al., 2021), for
our experiments (c.f. Section 5).

##  5\. Experiments and Results

In this section, we delineate an exhaustive analysis of our model’s
performance and also carry out a predictive comparison against the state-of-
the-art claim detection systems. We additionally analyze the predictions made
by our best model for out-of-sample instances from Twitter. Besides the
aforementioned, we conduct an ablation study to evaluate the substance values
affixed by each sub-module of our architecture.

Table 3. Macro F1 ($m\text{-}F1$) and Claim F1 ($c\text{-}F1$) of the competing models on different web-based datasets. The first two rows determine the effectiveness of DARÉ embeddings over the standard BERT representation. LESA (Gupta et al., 2021) is the current state-of-the-art system for all four datasets. Model | Twitter | Online Comments (OC) | Web Discourse (WD) | Micro Text (MT) | Average
---|---|---|---|---|---
| $m\text{-}F1$ | $c\text{-}F1$ | $m\text{-}F1$ | $c\text{-}F1$ | $m\text{-}F1$ | $c\text{-}F1$ | $m\text{-}F1$ | $c\text{-}F1$ | $m\text{-}F1$ | $c\text{-}F1$
K-means – BERT | 0.46 | 0.70 | 0.39 | 0.09 | 0.41 | 0.04 | 0.49 | 0.35 | 0.44 | 0.30
K-means – DARÉ | 0.52 | 0.83 | 0.39 | 0.16 | 0.47 | 0.11 | 0.50 | 0.21 | 0.47 | 0.33
BERT (Devlin et al., 2019) | 0.50 | 0.67 | 0.50 | 0.24 | 0.48 | 0.23 | 0.75 | 0.69 | 0.56 | 0.46
XLNet (Yang et al., 2019) | 0.52 | 0.70 | 0.45 | 0.24 | 0.51 | 0.12 | 0.49 | 0.43 | 0.49 | 0.37
Accenture (Williams et al., 2020) | 0.48 | 0.15 | 0.44 | 0.16 | 0.34 | 0.11 | 0.48 | 0.28 | 0.44 | 0.18
Team Alex (Nikolov et al., 2020) | 0.70 | 0.88 | 0.46 | 0.23 | 0.60 | 0.34 | 0.75 | 0.64 | 0.63 | 0.52
LESA (Gupta et al., 2021) | 0.67 | 0.89 | 0.51 | 0.26 | 0.61 | 0.35 | 0.80 | 0.71 | 0.65 | 0.55
DESYR | 0.67 | 0.92 | 0.60 | 0.27 | 0.78 | 0.59 | 0.85 | 0.79 | 0.73 | 0.64

###  5.1. Experimental Setup

In this section, we layout the backdrop for our experiments and highlight the
key conditions and practices.

To compute the DARÉ embedding of size $100$, we use the open source
Sentiment140 corpus, comprising 1.6 million
tweets666https://www.kaggle.com/kazanova/sentiment140. We use stacked BiLSTMs
with 256 hidden units for both f-nets. To emphasize again, there are no shared
parameters between f-nets and f-netr. Additionally, to encode our query and
definitions in the d-net, we use a BiLSTM with 64 hidden units. We use pre-
furnished definitions proposed in our previous work (Gupta et al., 2021) to
harbour our d-net; we encipher d-netC with $10$ definitions, and d-netNC with
$8$ definitions.

To train our model, we proceed with a vocabulary size of $30k$, and a maximum
document length of 50. We use the Adam (Kingma and Ba, 2014) optimizer and the
sparse categorical focal loss (Lin et al., 2017). We train the model for $100$
epochs with a batch size of $32$ and exercise early stopping. Since most of
the datasets are unbalanced towards one class, we adopt the sampling technique
to alleviate the issue. We experiment with multiple sampling ratio (c.f. Table
4), and as a result of fine-tuning, we select a sampling ratio of 5:2 (claims:
non-claims) for the Twitter dataset based on foraging through the sampling
ratio search space. For consistency, we select a sampling ratio of 5:2 (non-
claims: claims) for the OC, WD and MT datasets as well. Note that the sampling
technique is in line with the previous neural attempts that procured best
results on a 1:1 ratio (Daxenberger et al., 2017; Gupta et al., 2021).
Additionally, to ensure the riddance of seed stochasticity and to incorporate
maximal data, we train for 5 random splits and average them using voting.
Also, to incorporate a more holistic weighting mechanism, we vote on
predictions across three different values (1, 2, 3) of the $\gamma$ parameter
in the focal loss. For evaluating the claim detection systems, we compute
claim-F1 ($c\text{-}F1$) and macro-F1 ($c\text{-}F1$) scores.

Another fact of importance is that with DESYR, we design an architecture which
is lighter in comparison to its previous state-of-the-art systems, especially
LESA. LESA churns approximately $111M$ model parameters, while our proposed
model has only $7M$ model parameters. Moreover, the standard XLNet and BERT-
based models require a few $100M$ parameters for the same.

As is evident, constructing models in adherence with the task statement at
hand can be equally as effective if not more, and at times can be accomplished
with a fraction of the compute.

###  5.2. Baseline Models

Due to highly subjective nature of claims, claim detection can more often than
not prove to be a demanding task even for humans let alone machines. As with
automated, neural (machine-based) claim detection, the problem becomes even
more acute in case of web-based short texts, that usually lack soundness in
their linguistic edifice. Most of the existing claim detection models
(including state-of-the-art systems) struggle with the accurate identification
of claims. To assess and contrast the performance of DESYR, we consider the
following systems as baselines:

  * •

BERT (Devlin et al., 2019): It is a bidirectional transformer-inspired auto-
encoder LM that we fine-tune for classification.

  * •

XLNet (Yang et al., 2019): Similar to BERT, this too is a bidirectional
transformer-inspired LM, the only difference being that this is an auto-
regressive LM. We fine-tune it for classification.

  * •

Accenture (Williams et al., 2020): The authors employed a fine-tuned RoBERTa-
based system and nabbed the first position in the CLEF-2020 claim detection
shared task (Barrón-Cedeño et al., 2020).

  * •

Team Alex (Nikolov et al., 2020): The system ranked second at CLEF-2020 shared
task. The authors proposed the fusion of RoBERTa-based features and Twitter
meta-data to detect claims.

  * •

LESA (Gupta et al., 2021): This is the state-of-the-art claim detection system
wherein the authors proposed a system that leverages part-of-speech and
dependency-based linguistic encoders in sync with a BERT-based encoder to
detect claims.

  * •

In addition, we also perform a simple K-means clustering-based evaluation. We
assign dataset points to one of the two clusters – claim and non-claim,
considering their BERT and Poincaré representations, separately.

###  5.3. Performance Comparison

We present our collated results in Table 3. To compute the base efficacy of
the DARÉ embedding, we compare it against the BERT embeddings (Reimers and
Gurevych, 2019) without proffering any external supervision. Please note that
the Sentence-Transformers package(Reimers and Gurevych, 2019) facilitates out-
of-box computation of BERT-based dense vector representations for
sentences777https://www.sbert.net/. We employ K-means clustering to segregate
the claim with non-claim clusters. We evaluate the test data points in both
clusters and report the results in the first two rows of Table 3. We observe
that DARÉ performs better than BERT on the Twitter dataset by a considerable
margin. We also perform K-means clustering on the remaining three datasets as
well and observe improvements in most of the cases. The improvements could
possibly be attributed to our trained distribution being closer to web-based
informal texts despite the training corpus being significantly smaller than
BERT’s (Wikipedia: $2,500$ million words, Book Corpus: $800$ million words).

Furthermore, we evidently observe that DESYR outperforms all the existing
baseline systems including the current state-of-the-art, LESA (Gupta et al.,
2021). On the Twitter dataset, DESYR obtains the foremost $c\text{-}F1$ score
in contrast to all other baseline systems – it accounts for a $+3.3\%$
improvement over LESA in $c\text{-}F1$. With the OC dataset, we find that all
the baseline systems including DESYR report low scores for claims. However,
DESYR does yield $m\text{-}F1$ of 0.60 (with $+9$ points improvement over
LESA’s performance) – suggesting it performs well for the non-claim class. Out
of the four datasets, we observe the highest relative improvement on the WD
dataset with $0.59$ $c\text{-}F1$ and $0.78$ $m\text{-}F1$, translating to a
climb of $68.5\%$ and $27.8\%$ over LESA, respectively. On the MT dataset as
well, we observe an increment of $11.26\%$ in $c\text{-}F1$ and $6.25\%$ in
$m\text{-}F1$.

On average, DESYR improves the state-of-the-art performance by $16.36\%$ in
$c\text{-}F1$ and by $13.3\%$ in $m\text{-}F1$. As a general observation, we
see how systems grounded in linguistics, such as DESYR and LESA outperform
large LMs like BERT and XLNet, which in turn goes to indicate the importance
of task-specificity in model adaptation.

Table 4. Ablation result for DESYR on the Twitter dataset. The symbol ($-$)
signifies the absence of respective module. Claim-F1 ($c\text{-}F1$), Non
Claim-F1 ($nc\text{-}F1$), Macro-F1 ($m\text{-}F1$) and Weighted-F1
($w\text{-}F1$) are reported. Sampling ratio shows our attempt to alleviate
the label skewness.

| Sampling ratio |  |  |  |
---|---|---|---|---|---
Ablation | [Claim:Non-Claim] | $c\text{-}F1$ | $nc\text{-}F1$ | $m\text{-}F1$ | $w\text{-}F1$
DESYR | Org | 0.92 | 0.38 | 0.65 | 0.85
2-6 | $[1:1]$ | 0.89 | 0.33 | 0.61 | 0.81
2-6 | $[5:2]$ | 0.92 | 0.41 | 0.67 | 0.86
$\quad-$ {focal loss, d-net} | $[1:1]$ | 0.85 | 0.36 | 0.60 | 0.78
2-6 | $[5:2]$ | 0.90 | 0.39 | 0.65 | 0.83
$\quad-$ {focal loss} | $[1:1]$ | 0.86 | 0.37 | 0.61 | 0.80
2-6 | $[5:2]$ | 0.91 | 0.41 | 0.66 | 0.84
$\quad-$ {DARÉ} $+$ {GloVe} | $[1:1]$ | 0.90 | 0.38 | 0.64 | 0.83
2-6 | $[5:2]$ | 0.91 | 0.36 | 0.63 | 0.84

Table 5. Error analysis of the outputs on various datasets. For comparison, we also show the predictions of the current state-of-the-art system, LESA (Gupta et al., 2021). Dataset |  Example | Gold | Prediction
---|---|---|---
DESYR | LESA
| $t_{1}$ |  RT @PirateAtLaw: No no no. Corona beer is the cure not the disease https://t.co/fnba2fr2m2 | claim | claim | non-claim
2-6 TWR | $t_{2}$ |  @zlj517 Vaccine development is urgent. Please let me know when an effective vaccine is completed in the world. I go get it and bring it to China. Wait for early development. #pichperfact | non-claim | claim | claim
2-6 | $t_{3}$ |  #China has just discovered #vaccine against #coronavirus. Thank you china | claim | claim | non-claim
| $t_{4}$ |  I usually walk around and attempt to read all the names on the old gravestones. | non-claim | claim | non-claim
2-6 OC | $t_{5}$ |  Casey Siemezko is Charlie in Young Guns…which bothers me because I keep wantin to see him without his beard/mustache and this is the closest thing to it, but he has his 3D glasses…cant win. | non-claim | claim | claim
2-6 | $t_{6}$ |  My only big smile moment was Kurt’s dad knowing Kurt was g*y since he was a t*t, lovely little scene, playing against the dad’s macho image. | claim | claim | non-claim
| $t_{7}$ |  I don’t know about the rest of Virginia, but in Northern Virginia we have excellent public schools. | claim | non-claim | non-claim
2-6 WD | $t_{8}$ |  I am a 12-year-old 6th grader turning 13 in May, so I know what it’s like. | non-claim | non-claim | claim
2-6 | $t_{9}$ |  Here is my experience and I hope it will help you with your decision: In preschool, I had some issues, just like your son. | non-claim | claim | claim
| $t_{10}$ |  That’s why Germany should not introduce capital punishment! | claim | non-claim | claim
2-6 MT | $t_{11}$ |  Alternative treatments should be subsidized in the same way as conventional treatments, since both methods can lead to the prevention, mitigation or cure of an illness. | claim | claim | non-claim
2-6 | $t_{12}$ |  Besides it should be in the interest of the health insurers to recognize alternative medicine as treatment, since there is a chance of recovery. | non-claim | claim | claim

###  5.4. Ablation Study

Given that we hinge our model around Twitter, we perform the ablation study by
hacking off individual components from DESYR one at a time, and thereafter, we
evaluate the same on the Twitter dataset (Gupta et al., 2021). We present the
ablation study results in Table 4. We report $c\text{-}F1$ along with
$m\text{-}F1$ and weighted-F1 ($w\text{-}F1$).

We draw the simplest variant of DESYR by dropping d-net and employing sparse
categorical cross-entropy in place of focal loss to train our model.
Additionally, along with the mentioned withdrawals, we train on a 1:1 sampling
in place of the 5:2 sampling. As can be seen in Table 4 (rows 4-5), we observe
an increase of 5 $c\text{-}F1$ points and 5 $m\text{-}F1$ points simply by
reverting back to the 5:2 sampling. We conduct another similar ablation, while
retaining d-net with categorical cross-entropy. We again observe an increase
of 5 $c\text{-}F1$ points and 5 $m\text{-}F1$ points on a reversion to the 5:2
sampling. It is worth mentioning that the previous benchmark on the Twitter
dataset applied a 1:1 sampling across all their experiments (Gupta et al.,
2021). We, however, espy worse results on using the same. We additionally
discern that in two distinct cases, the addition of d-net results in a boost –
we see a boost of 1 $c\text{-}F1$ point and 1 $m\text{-}F1$ point when
comparing ablations that differ in their residence of d-net.

Within DESYR we experiment with different sampling ratios, as is clearly
evident we outperform on the original sampling and the 1:1 sampling. We
observe that with the original sampling, we see competitive results on
$c\text{-}F1$, however, the same doesn’t hold true for $nc\text{-}F1$, thereby
the 5:2 sampling is a better fit, given that in addition to better results, it
also helps keep the skew subservient.

Furthermore, we detect that the variant of DESYR that comes without focal
loss, performs worse in comparison to DESYR; the $c\text{-}F1$ and the
$m\text{-}F1$ values drop by 1 point each. Another interesting ablation is
where we choose to initialize DESYR with GloVe (Pennington et al., 2014) in
place of DARÉ. We see that DESYR in its native state outperforms GloVe
initialization by 1 $c\text{-}F1$ point and 4 $m\text{-}F1$ points (rows 8-9).

###  5.5. Error Analysis

Glancing at Table 3, one can evidently infer that all the claim detection
systems including DESYR are still far from absolute and are therefore prone to
errors. To reinforce our point once again, owing to the highly impressionistic
nature of claims and folksiness on OSM sites, the detection of claims is
cumbersome. To qualitatively appraise the performance of DESYR, we perform
error analysis in this section. Table 5 highlights a few randomly sampled
instances from the Twitter, OC, WD, and MT datasets, along with their gold and
output labels as predicted by DESYR. For comparison, we analyze the
predictions from the state-of-the-art claim detection architecture, LESA
(Gupta et al., 2021) as well. In some cases, both DESYR and LESA fail to
identify claims; however, in most of the cases DESYR performed well. We
additionally report instances misclassified by DESYR and/or LESA.

Table 6. Human evaluation on random tweets (non-dataset examples). DESYR
reports macro-F1 of 0.60 and claim-F1 of 0.73 on 50 random samples.

|  Example | DESYR | Human
---|---|---|---
$t_{1}$ |  #CovidVaccine — Assam vaccinated only 15% residents, young struggle to book slots. NDTV’s Ratnadip Choudhury reports | claim | claim
$t_{2}$ |  We went from May 1 to May 17 in 2 day | claim | non-claim
$t_{3}$ |  Nearly 51 lakh COVID-19 vaccine doses will be received by states/UTs within next 3 days: Health ministry. | claim | claim
$t_{4}$ |  I STAND WITH HUMANITY #IndiaStandwithPalestine | non-claim | non-claim
$t_{5}$ |  Par for the course. As if we’d trust an internal review. We are asking for #COVIDPublicEnquiryNow | non-claim | claim
$t_{6}$ |  They claim they are no longer asking for Aadhaar/mobile to give food at Indira canteens. But our Youth Congress team found otherwise. And documented it. We will continue to expose this government’s lies. | claim | claim
$t_{7}$ |  A third Australian has died in India from COVID-19. The family members of 11,000 stranded Aussies are pleading for the government to bring them home. #9News | claim | claim
$t_{8}$ |  My heartfelt gratitude to the men in uniform who did not deter from putting their lives in danger saving the lives of our citizens under extreme conditions. | non-claim | non-claim
$t_{9}$ |  Interacted with doctors across India. They shared insightful inputs, based on their own experiences of curing COVID-19. The determination of our doctors during these times is remarkable! | claim | non-claim
$t_{10}$ |  Abolish unpaid internships. There is absolutely no valid reason that justifies why you’re having students work 40h/week and paying them nothing. | claim | claim

As underlined previously, on the Twitter dataset, we observe that DESYR
obtains better classification results in comparison to all other baseline
systems. In examples $t_{1}$ and $t_{3}$, we see that LESA misclassifies both
the examples. In comparison, DESYR identifies the assertion and classifies
them correctly. With mistakes being inevitable, we observe that most of the
misclassified samples are non-claim ($t_{2},t_{4},t_{5},t_{9},t_{12}$). A
potential reason could be the skewed nature of the Twitter dataset (Gupta et
al., 2021) – the dataset is imbalanced with a hulking predisposition towards
claims (the number of claims is greater even after the 5:2 sampling ratio).
The presence of the phrase, ‘effective vaccination is completed’ in example
$t_{2}$, drives the system to assert on it and incorrectly predicting it as a
claim. Example $t_{7}$ has a dearth of context and could possibly have been a
part of a bigger phrase, and on top of that it is not a statement that would
severely affect public opinion, which is presumably why DESYR misclassifies
it. The latter argument was based off the guidelines proposed in our previous
work (Gupta et al., 2021). Clearly, the gold label for $t_{9}$ indicates that
it is not a claim; however, within the realm of possibility, it does seem that
the person indirectly claims about having issues. It is possible that DESYR
misclassifies in this case owing to this dormant pattern. Sentence $t_{12}$
emphasizes a chance of recovery with an alternative medicine, i.e., they
report a medical fact to be true, albeit with chance. There exists a
possibility that DESYR and LESA interpret a chance of recovery using
medication as a claim, and therefore, misclassify it.

###  5.6. Human Evaluation in the Wild

Over time, OSM sites have emerged as the hub for short, unstructured pieces of
informal text, where the amount of slang and incoherence in writing is
generally more significant than other online platforms. Considering the prime
focus of DESYR, we tend to evaluate our DESYR’s performance against real-world
data to detect claims on the web. We collect 50 random samples from Twitter
and predict their labels (claim or non-claim) using DESYR. Note that we
collect these examples in the wild. Subsequently, we present the predictions
to three human evaluators888They are linguistics by profession; and their age
ranges between $24$-$45$ years. and ask them to verify the labels following
the claim annotation guidelines defined in (Gupta et al., 2021). Finally, a
majority voting was used to get the final gold-label for these 50 tweets. We
obtain an inter-annotator score (Fleiss kappa) of 0.76 among the three
evaluators.

We present some of the instances in Table 6. As expected, most of the claims
were correctly labelled by DESYR. Out of 50 samples, DESYR classifies 32
samples correctly. We observe $m\text{-}F1$ score of 0.60, while $c\text{-}F1$
score of 0.73. Along with that, our model marked some false positives at the
expense of its precision. This is not an ideal situation but a better scenario
than biasing towards false negatives where claims are wrongly classified as
non-claims.

Examples $t_{1}$ and $t_{3}$ are claims exhibiting some statistics. Our model
DESYR rightly captured these values and classified both the tweets as claims.
However, DESYR was unable to learn the importance of numerical features in
$t_{2}$, specially when they occurred several times within the text, where it
failed to interpret the significance of the numerical features and ended up
mislabelling the tweet. Following the claim guidelines, we published in our
prior work (Gupta et al., 2021), negating a false claim also accounts as a
claim. In example $t_{6}$, the user tries to negate a claim and further claims
to document the evidence. DESYR comprehends the assertions and rightly labels
as claim. Through example $t_{8}$, the user imparts his/her personal beliefs
that might or might not affect the public; thus, DESYR possibly interpreted it
as a personal opinion and marked it as non-claim. Examples $t_{7}$ and
$t_{10}$ encompass strong claim phrases ‘has died in India’ and ‘paying them
nothing’, respectively. Clearly, these two examples make strong social
assertions that would be of interest to a larger audience and is possibly why
it is labeled as claim. Finally, in example $t_{9}$, the user commends the
determination of doctors during the global pandemic situation and expressed
their experiences. This example would not fall under the claim category;
however, DESYR mis-classifies it as a non-claim possibly due to the presence
of the phrase ‘curing COVID-19’.

Our observations from the in-the-wild evaluation suggest that that DESYR can
assign labels to unseen tweets quite efficiently and accurately. Moreover, we
do not follow any unexpected behavior of DESYR. Thus, furnishing us with
empirical shreds of evidence that DESYR can be used for claim detection in
informal texts.

##  6\. Conclusion

Through this far-reaching and orderly study, we tend to make notable
contributions that will end up being considerable strides in the field of
claim detection. We epitomized Poincaré embeddings with the NLP task, which
showed promising results for the claim detection task. The proposed model,
DESYR, determined the existence of claims in the online text by aligning the
query to encoded definitions and projecting them into a purer space. We
evaluated DESYR across four web-based datasets, which comprise short informal
texts, and observed up to par results. The comparative investigation espoused
the more nuanced performance of our model compared against various existing
systems. Experiments demonstrated the superiority of our model with $\geq$3%
$claim\text{-}F1$ improvements over the existing state-of-the-art claim
detection system, LESA. Additionally, we exhibited every individual
component’s performance and significance in our model through an exhaustive
ablation study. Finally, we showed the robustness of DESYR through a
qualitative human evaluation in the wild on 50 random samples.

##  7\. Acknowledgement

The work was partially supported by the Accenture Research Grant. T.
Chakraborty would like to acknowledge the support of Ramanujan Fellowship,
CAI, IIIT-Delhi and ihub-Anubhuti-iiitd Foundation set up under the NM-ICPS
scheme of the Department of Science and Technology, India.

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