Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction (2023)

Abstract: We present Claim-Dissector: a novel latent variable model for fact-checking and analysis, which given a claim and a set of retrieved evidence jointly learns to identify: (i) the relevant evidences to the given claim (ii) the veracity of the claim. We propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way — the final veracity probability is proportional to a linear ensemble of per-evidence relevance probabilities. In this way, the individual contributions of evidences towards the final predicted probability can be identified. In per-evidence relevance probability, our model can further distinguish whether each relevant evidence is supporting (S) or refuting (R) the claim. This allows to quantify how much the S/R probability contributes to final verdict or to detect disagreeing evidence. Despite its interpretable nature, our system achieves results competetive with state-of-the-art on the FEVER dataset, as compared to typical two-stage system pipelines, while using significantly fewer parameters. Furthermore, our analysis shows that our model can learn fine-grained relevance cues while using coarse-grained supervision and we demonstrate it in 2 ways. (i) We show that our model can achieve competitive sentence recall while using only paragraph-level relevance supervision. (ii) Traversing towards the finest granularity of relevance, we show that our model is capable of identifying relevance at the token level. To do this, we present a new benchmark TLR-FEVER focusing on token-level interpretability — humans annotate tokens in relevant evidences they considered essential when making their judgment. Then we measure how similar are these annotations to the tokens our model is focusing on.

## introduction today's automated fact-checking systems are moving from predicting the claim's veracity by capturing the superficial cues of credibility, such as the way the claim is written, the statistics captured in the claim author's profile, or the stances of its respondents on social networks @xcite @xcite @xcite towards evidencegrounded systems which, given a claim, identify relevant sources and then use these to predict the claim's veracity @xcite @xcite . in practice, providing precise evidence turns out to be at least as important as predicting the veracity itself. disproving a claim without linking it to factual evidence often fails to be persuasive and can even cause a "backfire" effect -refreshing and strengthening the belief into an erroneous claim @xcite 2 . for evidence-grounded fact-checking, most of the existing state-of-the-art systems @xcite @xcite employ a 3-stage cascade approach; given a claim, they retrieve relevant documents, rerank relevant evidences (sentences, paragraphs or larger text blocks) within these documents, and predict the claim's veracity from the top-k (usually @xmath0. this comes with several drawbacks; firstly, the multiple steps of the system lead to error propagation, i.e. the input to the last system might often be too noisy to contain any information. some previous work focused on merging evidence reranking and veracity prediction into a single step @xcite . secondly, in opendomain setting, number of relevant evidences can be significantly larger than k 3 , especially when there is a lot of repeated evidence. thirdly, in open-domain setting, sometimes there is both, supporting and refuting evidence. the re-ranking systems often do not distinguish whether evidence is relevant because it supports or refutes the claim, and thus may select the evidence from one group based on the in-built biases. to further strengthen the persuasive effect of the evidences and understand the model's reasoning process, some of these systems provide cues of interpretability @xcite . however, the interpretability in the mentioned work was often considered a useful trait, which was evaluated only qualitatively, as the laborintensive human evaluation was out of the scope of their focus. to this extent, we propose claim-dissector (cd), a latent variable model which: finally, we collect a 4-way annotated dataset tlr-fever of per-token relevance annotations. this serves as a proxy for evaluating interpretability: we measure how similar are the cues provided by the model to the ones from humans. we believe future work can benefit from our quantitative evaluation approach while maintaining focus. ## model description we present a 2-stage system composed of the retriever and the verifier. the documents are ranked via retriever. each document is split into blocks. the blocks from top-ranking documents are passed to verifier and jointly judged. our interpretable cd verifier is capable of re-ranking documents for any granularity of relevant evidence (e.g., document, block, sentence, token). jointly, the same model predicts the claim's veracity. the overall schema of our approach is depicted in figure 1 . ## experimental setup unless said otherwise, we employ debertav3 @xcite as lrm. in all experiments, we firstly pre-train model on mnli @xcite . we use maximum block-length l @xmath1. our recipe for implementation and model training is closely described in appendix n. ## results we report results of base-sized models based on 3-checkpoint average. we train only a single large model. we further evaluate retrieval in appendix m as it is a non-essential part of our contribution. performance. we compare the performance of our system with previous work in we observe that (i) even our base-sized roberta-based cd model outperforms base-sized hesm on dev data, and its large version outperforms large-sized kgat, dream and hesm on test data, (ii) our base sized debertav3-based cd model matches large-based dream and even kgat with oracle inputs on dev set, (iii) model version with hyperlink expansion (suffixed \w he) further improves the overall performance, (iv) using larger model improves mostly its accuracy, (v) claim-dissectorl \w he achieves better fever score than t5-based approach (with 12 we consider mask-class logits as scores for masker. ## conclusion in this work, we proposed claim-dissector, an interpretable probabilistic model for fact-checking and fact-analysis. our model jointly predicts the supporting/refuting evidence and the claim's veracity. it achieves state-of-the-art results, while providing three layers of interpretability. firstly, it identifies salient tokens important for the final prediction. secondly, it allows disentangling ranking of relevant evidences into ranking of supporting evidence and ranking of refuting evidence. this allows detecting bipolar evidence without being exposed to such bipolar evidence sets during training (appendix d). thirdly, it combines the pertoken relevance probabilities via linear combination into final veracity assessment. therefore it can be identified to what extent the relevance of each token/sentence/block/document contributes to final assessment. conveniently, this allows to differentiate between the concept of evidence relevance and its contribution to the final assessment. our work was however limited in experiments with these coefficients, and we would like to analyze what they can learn, and how to inject features, such as satire assessment or source trustworthiness, through these coefficients in our future work. finally, it was shown that a hierarchical structure of our model allows making predictions on even finer language granularity, than the granularity the model was trained on. we believe the technique we proposed is transferable beyond fact-checking.