# ExClaim: Explainable Neural Claim Verification Using Rationalization

Sai Gurrapu1, Lifu Huang1, Feras A. Batarseh2
1Department of Computer Science, 2Department of Biological Systems Engineering
(BSE)
Virginia Tech
Blacksburg, United States
{saig, lifuh<EMAIL_ADDRESS>
###### Abstract

With the advent of deep learning, text generation language models have
improved dramatically, with text at a similar level as human-written text.
This can lead to rampant misinformation because content can now be created
cheaply and distributed quickly. Automated claim verification methods exist to
validate claims, but they lack foundational data and often use mainstream news
as evidence sources that are strongly biased towards a specific agenda.
Current claim verification methods use deep neural network models and complex
algorithms for a high classification accuracy but it is at the expense of
model explainability. The models are black-boxes and their decision-making
process and the steps it took to arrive at a final prediction are obfuscated
from the user. We introduce a novel claim verification approach, namely:
ExClaim, that attempts to provide an explainable claim verification system
with foundational evidence. Inspired by the legal system, ExClaim leverages
rationalization to provide a verdict for the claim and justifies the verdict
through a natural language explanation (rationale) to describe the model’s
decision-making process. ExClaim treats the verdict classification task as a
question-answer problem and achieves a performance of 0.93 F1 score. It
provides subtasks explanations to also justify the intermediate outcomes.
Statistical and Explainable AI (XAI) evaluations are conducted to ensure valid
and trustworthy outcomes. Ensuring claim verification systems are assured,
rational, and explainable is an essential step toward improving Human-AI trust
and the accessibility of black-box systems.

###### Index Terms:

Natural Language Processing, Rationalization, Claim Verification, Explainable
AI, Language Modeling

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##  I Introduction

Figure 1: ExClaim Approach Example

Information on the web has grown remarkably in the recent few decades, and
misinformation has become a significant challenge. Misinformation can weaken
public trust in institutions, and economies. For instance, in 2013 Associated
Press published a false tweet claiming that President Barack Obama suffered
injuries from an explosion in the White House and within seconds S&P 500 lost
$130 billion in stock value [1]. The impact of such information is not only on
global economies but also on all aspects of our lives since it has become
commonplace to consume news digitally. A leading cause for the rise of
misinformation is that it can now be created cheaper and distributed faster
with the internet when compared to traditional platforms such as newspapers
and television [2]. The growth of social media usage has also allowed
effortless mass sharing of information.

The recent progress with Language Models (LM) in Natural Language Processing
(NLP) has made it even easier, cheaper, and faster for a machine to generate
artificial text [3]. Generative LMs have become sophisticated such as GPT-3 at
text generation, and their results are almost on par with human-written text
in terms of readability, coherence, and grammatical accuracy [4]. In [5], the
authors demonstrate that LMs are improving 10x every year in terms of
parameters and model size and that there is an incredible amount of progress
with natural language text generation that is bound to happen. There is a
possibility that these models will reach (or even surpass) human performance
in a few years.

Such advances can likely lead to more rampant misinformation than what we have
today online. There has been a growing interest in identifying the
authenticity and truthfulness of online content. Professional fact-checkers
and independent fact-checking organizations exist, but their work cannot be
scaled linearly with the amount of digital content growth. This led to an
increase in interest in automated fact-checking, also known as claim
verification [6]. By fusing deep learning techniques with complex networks and
algorithms, claim verification systems have achieved respectable performance
but have become black-boxes. Optimizing for the task performance comes at the
expense of model explainability. The developers of the model and the end-users
which we refer to as the nonexperts do not fully know the internals or
understand its decision-making processes. Furthermore, [6] points that there
exists no system that offers explanations for sub-tasks in the claim
verification pipeline.

Currently, a claim is fed into the system, the model checks against an
evidence dataset, and it returns a binary verdict as the output; true if the
evidence supports the claim otherwise false. This approach has become
commonplace [7]; however, many questions still remain unanswered. What
evidence influenced the prediction? Is the evidence trustworthy? How can the
nonexpert understand the model internals? These are all missing components of
the process and there are no wide standards to assure the outcomes of these
black-box systems [8].

Explainability techniques are available but they are insufficient because many
require specialized domain knowledge to understand. Claim verification systems
are tools which would generally be used by a wide public audience. They need
to be explainable in the sense that they appropriately justify their outcomes
and are comprehensible to the nontechnical users. Local Interpretable Model-
agnostic Explanations (LIME), Attention scores and saliency heatmaps are
generally helpful to the model developer, but not the end-user.

An emerging NLP explainable technique is called Rationalization [9]. It
provides a rationale also known as a natural language explanation (NLE) to
justify a model’s prediction. The reasoning behind a model’s prediction can be
understood simply by reading the rationale, thereby revealing the model’s
decision-making process. Rationalization can be an attractive explainable
technique because it is intuitive and human-comprehensible, and does not
require domain knowledge [10]. It is a type of local explanation because there
is a unique explanation for each prediction [11]. Rationalization frames
explainability as an outcome-explanation problem. It looks at explainability
from the perspective of an end-user whose aim is to understand how the model
arrives at its final outcome [12]. This process is encapsulated in the
rationales statement.

Rationales are also widely used in the legal system. A judge rules a verdict
and it cannot be upheld without a legal opinion which is a written explanation
laying out the rationale for a ruling. Inspired by this, we propose a novel
approach, namely, ExClaim which leverages rationalization for an explainable
neural claim verification system that is accessible and trustworthy to the
nonexpert. It can not only predict a verdict but also defend and rationalize
its output as a NLE. We also introduce NLP Assurance and incorporate extensive
explainable evaluations to assure the verdict and NLE outcomes are valid to
help reinforce Human-AI trust [13]. The following are the contributions of
this paper:

  1. 1.

Introduces a new benchmark claim verification dataset with credible
foundational information and a novel explainable claim verification approach
called ExClaim.

  2. 2.

Uses transfer learning and presents an effective method to generate
abstractive rationales in an unsupervised manner without any ground truth.

  3. 3.

Treats the verdict classification task as a question-answer problem and
demonstrates that it can significantly help improve the classification
performance.

  4. 4.

Presents the problem of claim verification through the lens of explainability
and is the first to introduce sub-task explanations in the claim verification
space.

The structure of this paper is as follows: in Section 2, we present related
work. In Section 3, we introduce a new benchmark claim verification dataset.
In Section 4, we demonstrate our experiment methodology, while Section 5
describes experimental results. Lastly, in Section 6, we provide a conclusion
and future directions.

##  II Related Work

Several studies have been conducted on FEVER, LIAR, and MultiFC datasets, the
most extensive datasets available for claim verification. FEVER does not
contain real-world claims; instead, they are generated from Wikipedia. MultiFC
uses the top k search results from Google as its evidence for claims which is
not reliable, and quality could vary drastically. Hence, we disregarded them
in the study and focused on Politifact-based data. The LIAR dataset by [14]
which was the first large-scale claim verification Politifact dataset, which
led to an increase in research in this field. It is a six-way classification
dataset, and [14] presents a Convolutional Neural Network (CNN) model that
uses the claim and metadata such as speaker, party, and state, amongst others,
to achieve a 0.277 accuracy. Using metadata could potentially introduce
unwanted biases towards certain parties and groups. The model will be
influenced by those details than using the evidence for a claim. In [15], the
authors use an attention mechanism on the speaker and topic and achieve a 0.41
accuracy.

In [16], the authors publishe their LIAR-PLUS dataset with human-written
justifications from each Politifact article, and their model uses a P-BiLSTM
with claim and justification as inputs and achieves a 0.70 on binary
classification on the Politifact data. The current SOTA performance on binary
verdict classification on LIAR-PLUS is by [17], and their siamese network
achieves a 0.82 accuracy. Although these datasets are different from the
ExClaim dataset, they shed light on the progress of verdict classification
using Politifact claims. ExClaim serves as a new benchmark dataset with more
credible information, and our results from Section 5 serve as the new
baselines.

In the previously discussed Politifact work, the role of explainability and
methods to assure the black-box predictions are untouched. A majority of
explanation methods in the broader claim verification space are quantitative.
Papers [18], [19], [20], [21], and [22] all propose attention-based
explanations using BiLSTMs, LSTMs, CNNs, co-attention networks, and decision
trees, respectively. In [23] and [24], they both use rule discovery to derive
knowledge graphs and Horn rules explanations. Given the low accessibility of
these explanation methods, [25] in 2020 presents the first study that jointly
explains and predicts a verdict using a rationale. They use a supervised
technique using DistilBERT for rationale extraction and perform an annotation
task to evaluate rationale quality and interpretability. In [26], the authors
demonstrate this technique on a public health claims dataset. The progress of
natural language explanations as an explainability technique has been minimal
and constrained to supervised methods.

##  III ExClaim Foundational Dataset

TABLE I: ExClaim Foundational Dataset Statistics Dataset Statistics |
---|---
Total Articles Size | 4006
Training Set Articles Size | 2804
Validation Set Articles Size | 601
Test Set Articles Size | 601
Total Support Labels | 2013
Total Refute Labels | 1993
Average Token Length of Claim | 17
Average Token Length of Evidence | 449

In [6], the authors note that the availability of datasets for experimentation
is the current bottleneck with automatic claim verification. The two datasets
that we first considered were LIAR [14] and LIAR-PLUS [16]. The datasets were
developed by scraping Politifact, an independent fact-checking platform.
However, they were not sufficient for our task because we found problems with
the content with a few features missing that we required. Every Politifact
article includes a claim, a six-class verdict, ruling comments written by a
human fact-checker; a short justification summary at the end of the article,
and metadata such as speaker, subject, and other components. In the LIAR and
LIAR-PLUS datasets, they exclude the ruling comments, and previous work relies
primarily on using the justification for claim verification. Frequently, this
justification summary misses essential information from the verdict comments
and serves as more of a closure to the article. Therefore, the datasets were
not a reliable structure for our task.

We developed a new claim verification dataset using Politifact for our
experiment that includes information on 4000+ articles as shown in Table I.
Our dataset consists of the following features: Claim, Date, Source, Verdict,
and the ruling comments we refer to as the Evidence and the URL. Determining
the authenticity and ensuring that the evidence is neutral is crucial for
claim verification. Suppose the evidence includes polarizing language and has
a strong True or False sentiment. In that case, the model uses those features
to predict a verdict instead of truly learning the evidence content.
Additionally, Politifact often cites mainstream news mediums as sources. Most
of the time, these sources are heavily biased and skew to the left or right
with their content production. Due to these factors, data cleaning is
performed based on the following criteria.

When verifying claims, credible and trustworthy evidence is essential. Paper
[8] points that evidence is primarily available from reputable institutions
such as world governments and international organizations. News sources are
mostly avenues of secondary sources. We identified the top 30 most popular
news platforms and removed evidence paragraphs citing them. Our evidence
includes primarily information from the U.S. government (State and Federal
Laws, and government departments and agencies such as Centers for Disease
Control and Prevention, Bureau of Labor Statistics, and Census Bureau amongst
others) and also international organizations (United Nations, World Health
Organization, World Trade Organization and others). Politifact includes six
classes for the verdict (True, Mostly True, Half True, Mostly False, False,
Pants on Fire). We only scraped articles with True or False labels since the
accuracy for the other classes was not clear. Claim verification systems that
output True or False as the verdict can be misleading because there is always
a possibility for error. We pre-process the verdict labels in our dataset and
convert True and False into Supports and Refutes, respectively. From the end
user’s perspective, this approach makes it intentional that the evidence at
hand either supports or refutes the claim. The end user has the option to
conclude if the claim is true or false from the given outputs. We release this
as the ExClaim Foundational dataset, a new benchmark claim verification
dataset with foundational sources222https://github.com/AI-VTRC/ExClaim.

Figure 2: ExClaim Architecture

##  IV Methods

The Principle of Factor Sparsity also known as the Pareto principle states
that for many outcomes in a system, 80% of the effects come from 20% of the
causes [27]. This principle generally holds true in many situations and has
been successfully applied in various disciplines such as medicine,
engineering, and economics [28, 29]. In computer science, it was shown useful
for the optimization of genetic algorithms [30]. It has also been applied in
software testing and [31] demonstrates that on average ”20% of the code has
80% of the errors”.

Inspired by the Pareto principle, we hypothesize that on average only 20% of
the information in the evidence is important to verify a claim and the
remainder 80% is nonessential. This is similar to how humans approach manual
claim verification. There are a few key pieces of information in the evidence
that most influence our decision and compel us to rule that a claim is either
true or false. Paper [32] categorizes this as an explain-then-predict model
and [33, 34, 35] cite improvements in task performance optimization after
using similar techniques. This lays the foundation for our ExClaim approach as
illustrated in Figure 2.

###  IV-A Rationale Generation

To determine the salient information in the evidence, we use transfer learning
and treat it as an unsupervised summarization task by using language models.
Summarization techniques have been shown to distill the most important
information from a given source with high accuracy [36]. We specifically use
abstractive summarization because extractive techniques lack coherence,
readability, and overall quality that does not reflect human-written summaries
[37, 38]. We refer to the generated summary as rationales which later become
part of the NLE to justify the verdict prediction.

Language models perform well at summarization as first demonstrated by [39]
where a BERT-based abstractive summarization model outperforms most non-
Transformer-based models. However, a majority of the current language models
have a maximum sequence length of 512 tokens. As Table I shows, the average
sequence length of the evidence is 449 tokens with some articles over the 512
token limit. To minimize truncation, we leverage the BART language model which
has a maximum sequence length of 1024 tokens and it outperformed previous work
on summarization at its publication [40]. BART is pre-trained on the English
language and we specifically use the bart-large-cnn checkpoint from Hugging
Face that has been fine-tuned on a large corpus of text-summary pairs [41]. We
learn the function $f(E)=P^{R}$ where, given the evidence
$E=\\{E_{0},E_{1},…,E_{n}\\}$, it predicts the paragraph of rationales where
$P^{R}=\\{P^{R}_{0},P^{R}_{1},…,P^{R}_{n}\\}$. The terms $E_{i}$ and
$P^{R}_{i}$ are the i-th words of the evidence and the rationales paragraph,
respectively.

The function $f(E)$ tokenizes the input E and passes it to a bidirectional
encoder, and $P^{R}$ is generated through an autoregressive decoder on the
final hidden layer. We use this model to perform inference on each evidence
sample in the train, validation, and test sets to generate their respective
rationales. The output $P^{R}$ is set to a maximum sequence length of 120
tokens and a minimum of 75 tokens which we found to be optimal after
experimentation. The claim was not an input in this process and it is to
ensure that the rationales are balanced and are not heavily biased towards any
one side of the verdict.

###  IV-B Verdict Classification

In [42], the authors demonstrate the benefits of multitask learning where
downstream tasks are viewed as a question-answer problem using pre-trained
language model. Based on our findings, this method has not been explored in
the claim verification domain. Given its promising results in other domain, we
frame the ExClaim classification task as a question-answer problem.

We leverage a pre-trained T5 model, a text-to-text generation model [43] with
SOTA results on many NLP tasks including question-answering. We specifically
use T5’s capability with the Choice of Plausible Alternatives (COPA) task.
COPA was introduced by [44] and it is a type of question-answering problem
that examines automated commonsense causal reasoning. The objective is given a
question, a premise as context, and two answer choices perform binary
classification to determine the correct answer choice. For this task, we
conceive a function $g(S)=P^{V}$ where $S$ is the input prompt sequence and
$P^{V}\in\\{V^{S},V^{R}\\}$ is the binary verdict prediction where
$V^{S}=\\{Supports\\}$ and $V^{R}=\\{Refutes\\}$. We formulate $S$ as the
following where the claim as the question $C=\\{C0,C1,…,Cn\\}$ with $Ci$
indicating the i-th word in the claim, the generated paragraph of rationales
as the premise $P^{R}$, and the possible verdicts as $V^{S}$ and $V^{R}$. The
sequence $S$ is fed in as “copa choice1: $V^{S}$ choice2: $V^{R}$ premise:
$P^{R}$ question: $C$”.

We fine-tune the T5 model on our train dataset with the sequence $S$ for each
sample. We set the batch size to 8, cross-entropy as the loss function and
AdamW as the optimizer with a 2e-5 learning rate and we train our model for 20
epochs [45]. We evaluate the model’s performance on the validation set at
every 350 steps. A majority of the input sequences were below T5’s maximum
sequence length of 512 tokens and if the length of S went over the limit T5
does not truncate automatically since T5 uses relative positional bucketing or
relative attention [46]. It continues to work until it is exhausted of memory
which never occurred during our experimentation.

####  IV-B1 Baselines

Figure 3: SHAP Explanation [47]

We also utilize other architectures as baselines and they are as follows.

LSTM Previous work such as [14, 16] have relied heavily on LSTM networks for
the verdict classification. We designed a multi-input LSTM classification
model. It takes the claim and evidence as two inputs which are converted into
GloVe embeddings and are individually passed through an embedding layer and an
LSTM layer with 128 features and the outputs of these are concatenated and
passed through a dense layer with ReLu activation and a final dense layer with
two neurons and softmax activation for the binary prediction output.

DistilBERT The DistilBERT model is also used often in the claim verification
space. We fine-tune a pre-trained DistilBERT model. We set the batch size to
8, enable a sliding window to prevent truncation, use AdamW for optimization,
and train for 2 epochs.

BART We use a BART Zero-Shot classification model, specifically, the fine-
tuned bart-large-mnli model checkpoint from Hugging Face and we directly
perform inference with a zero-shot problem setup.

###  IV-C Natural Language Explanation

The NLE is a generative task using a language model. However, during
experimentation with SOTA natural language generation models such as GPT-2
[48] and GPT-3 [49], there were challenges with respect to language model
hallucination. The model hallucinates by introducing unintended and irrelevant
text in the generation process. Paper [50] suggests that such problems
diminish user trust and fail to meet their expectations. Especially for a
claim verification system, introducing unintended text makes the entire system
worthless and can lead to dangerous consequences. Instead, we used abstractive
summarization to generate the rationales, originally it was extractive. With
abstractive, the rationales reflect the natural style of a human-written text.
We formulate the NLE by concatenating the claim C, the rationales $P^{R}$, and
the verdict $P^{V}$. The resulting NLE, $P^{N}$, is as follows “The evidence
$P^{V}$ the claim because $P^{R}$”.

##  V Results and Discussion

In this section, we describe the experimental results with supporting
analysis.

###  V-A SHAP Explanation

The process of extracting the rationales is an intermediate step in the
ExClaim approach. There is also no available ground truth data to assess its
accuracy. We used an explainable technique called SHAP, which leverages game
theory to explain the output of any ML model [47]. SHAP is based on Shapley
values in economics which measure the marginal contribution of a feature
towards the final outcome of a system. These values are computed by
meticulously perturbing the input features and observing how those changes
affect the final prediction of the model. While performing inference to
generate the rationales, we also pass our model to a SHAP explainer. Figure 3
shows the output explanation where the explainer provides Shapley values for
input features in the text that influenced the generation of $P^{R}_{i}$ in
$P^{R}$. The red highlights indicate features with negative Shapley values,
which are negative contributions, and similarly, blue represents positive
contributions and values. SHAP is an accessible explanation method for the
nonexpert because the explainer illustrates the marginal influence of each
component by visualizing the Shapley values with colored highlights.

TABLE II: Macro F1 Scores for Verdict Classification Models Model | Valid | Test
---|---|---
BART Zero-Shot | - | 0.58
LSTM | 0.55 | 0.47
DistilBERT | 0.82 | 0.81
T5 | 0.85 | 0.85
ExClaim T5 | 0.92 | 0.93

###  V-B Verdict Classification

We measure the accuracy for our verdict classification models using the macro
F1 score. The models we have experimented with and their results for
validation and test datasets are shown in Table II. Since the BART Zero-Shot
was not finetuned on this dataset, we only include the F1 score from the test
set inference. Our approach using ExClaim T5 COPA outperforms the baselines,
specifically, the DistilBERT model, by 10 points on the validation and 12
points on the test set. An advantage of language models is that they are pre-
trained on existing knowledge. When fine-tuned on downstream tasks, they can
use transfer learning and leverage the pre-existing data to improve task
performance. Language models such as BART, DistilBERT, and T5 were pre-trained
on vast amounts of internet data, and it hints at why the pre-trained language
models performed better than the LSTM model. Amongst the language models,
zero-shot classification performed poorly, and a potential reason for this is
that fine-tuning generally helps improve task performance and accuracy.

TABLE III: NLI Evaluation Results NLI Result | Count | Percentage
---|---|---
Entailment | 168 | 27.9%
Neutral | 253 | 42.0%
Contradiction | 180 | 29.9%

###  V-C Natural Language Inference

The NLE is an open-ended text without ground truth data. This eliminated any
possibility of using the ROUGE-N or BLEU score for evaluation. The work by
[51] demonstrates that Natural Language Inference (NLI) can be confidently
used to evaluate the correctness of abstractive summarization results without
any human-written references. In [52], they claim that assessing the accuracy
of the textual entailment can be a reasonable evaluation method. NLI attempts
to determine given a premise if the hypothesis is true (entailment), false
(not entitlement/contradiction), or undetermined (neutral) [53].

We use the NLI textual entailment technique to automatically evaluate our NLE.
We used a pre-trained T5-large model, specifically, the CB task, which was
fine-tuned on [54]’s NLI dataset. We experimented with other T5 NLI tasks such
as QNLI, MNLI, and RTE, and we found that CB had the best performance. For
each generated NLE, $P^{N}$, for the test set, we formulate the NLI input for
our model as follows: “cb hypothesis: $C$ premise: $P^{N}$”. Essentially, we
test can we logically deduce the claim from the NLE. The results are shown in
Table III. Neutral NLEs is the majority with 42% and contradiction and
entailment as the following. A potential reason for this is we are currently
relying on transfer learning for the NLI evaluation, and it has not been fine-
tuned on our dataset. Identifying and establishing a logical connection
between the NLE and the claim can be challenging. An alternate reason is that
the quality of the rationales may be sufficient enough to not be a
contradiction the majority of the time but also not be sufficient enough for
the premise to entail the hypothesis, which may be why neutral is the
majority.

###  V-D Manual Evaluation

In addition to the automatic evaluation with NLI, we also conduct a manual
evaluation of the generated NLEs to assess its quality. For the evaluation
setup, we randomly select 100 claims and its respective NLE instances from our
test set. We had three annotators who were shown the claim and its NLE and
their task was to judge for the following criteria: Plausibility, Fluency, and
Correctness. The annotators were required to rely only on the information
provided with each working individually. Since non-expert annotators might
find it difficult to give thoughtful judgment, we adopt a rating scale from 1
to 5 for each criterion as presented in IV.

We define plausibility as how convincingly can the NLE explain the verdict
prediction [55] and its rating scale is adopted from [56]. Fluency is defined
by [57] as having correct spelling, good grammar, flow, and tone and its
rating scale is adopted from [57]. Correctness is defined as the likelihood of
the NLE and the verdict being true and its scale is from [56].

TABLE IV: Manual Evaluation Criteria and Rating Scale Plausibility | Rating
---|---
Very Convincing | 5
Slightly Convincing | 4
Slightly Not Convincing | 3
Not Convincing | 2
Can Not Judge | 1
Fluency | Rating
Flawless English | 5
Good English | 4
Non-native English | 3
Disfluent English | 2
Incomprehensible | 1
Correctness | Rating
Absolutely True | 5
Probably True | 4
Probably Not True | 3
Absolutely Not True | 2
Can Not Judge | 1

Figure 4 shows the results for the manual evaluation. We use DistilBERT as a
comparison since it is used often in the claim verification space [25]. On
average, ExClaim outperformed DistilBERT in all of the ratings. A major
contributor is that ExClaim is an ensemble approach that leverages two strong
language models (BART and T5) and is, therefore, better able to work in our
dynamic context of rationale creation, verdict classification, and NLE
generation. Both approaches have the same criteria ranking where fluency has
the highest rating, then plausibility, and lastly correctness. This is
expected for fluency since language models generally work well at producing
proper text without major grammatical errors. Plausibility was the lowest on
average and it indicates that the rationales chosen could be more persuasive.
DistilBERT has a lower rating for Plausibility and it may be due to its
limitations with text summarization. The BART model used in ExClaim
outperforms BERT-based models at text summarization that we used for the
rationale generation. Further, DistilBERT is extractive summarization and this
creates challenges with the coherency and convincingness of the rationales.
Correctness on average demonstrates that there is a decent likelihood that the
rationales presented with the verdict are accurate for the claim. The T5 model
in ExClaim excels at transfer learning and is, therefore, better able to
classify the verdict with respect to the rationales presented at inference
time. In ExClaim, the verdict and rationales may have strong entailment in the
NLE whereas with DistilBERT they may be more random and contradicting.

Figure 4: ExClaim Manual Evaluation

##  VI Conclusions

In this paper, we introduced a new benchmark dataset with foundational
information (from government policy documents and international
organizations), and we presented promising baseline results on the verdict
classification task using ExClaim’s question and answer approach. We also
demonstrated an unsupervised method of using transfer learning and abstractive
summarization to generate rationales which are used to determine the verdict
and generate an NLE. We showed multiple explainability methods to assure the
final outcomes and also the intermediate sub-tasks outcomes which has not been
done before in a claim verification system. We presented a novel claim
verification approach and approached the problem with rigorous explainability
to ensure the system is accessible, transparent, and trustworthy to the
nonexpert.

Certain limitations do exist such as the verdict prediction and NLE being
heavily impacted by the quality of the foundational data available.
Additionally, claims also need to be closely related to the information
included in the foundational dataset. Many promising future work directions
exist to further progress in explainable claim verification. For example,
developing better abstractive methods for the NLE generation from a given
claim and rationales and avoiding LM hallucination. Obtaining the evidence
directly from the sources listed on Politifact can yield more plausible
rationales than relying on the ruling comments. Rationale generation technique
without summarization has also been explored very little. Lastly,
incorporating AI assurance methods as demonstrated by [13] in NLP is also a
viable path for improving general model trustworthiness.

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