Improving Evidence Retrieval for Automated Explainable Fact-Checking (2021)

Abstract: Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines.

## introduction with the emergence of social media and many individual news sources online, the spread of misinformation has become a major problem with potentially harmful social consequences. fake news can manipulate public opinion, create conflicts, elicit unreasonable fear and suspicion. the vast amount of unverified online content led to the establishment of external post-hoc fact-checking organizations, such as politifact, factcheck.org, snopes etc, with dedicated resources to verify claims online. however, manual fact-checking is time consuming and intractable on a large scale. the ability to automatically perform fact-checking is critical to minimize negative social impact. automated fact checking is a complex task involving evidence extraction followed by evidence reasoning and entailment. for the retrieval of relevant evidence from a corpus of documents, existing systems typically utilize traditional sparse retrieval which may have poor recall, especially when the relevant passages have few overlapping words with the claims to be verified. dense retrieval models have proven effective in question answering as these models can better capture the latent semantic content of text. the work in @xcite is the first to use dense retrieval for fact checking. the authors constructed a new dataset called factual-nli comprising of claim-evidence pairs from the fever dataset @xcite as well as synthetic examples generated from benchmark question answering datasets @xcite . they demonstrated that using factual-nli to train a dense retriever can improve evidence retrieval significantly. while the fever dataset has enabled the systematic evaluation of automated fact-checking systems, it does not reflect well the noisy nature of real-world data. motivated by this, we introduce the factual-nli+ dataset, an extension of the fever dataset with synthetic examples from question answering datasets and noise passages from web search results. we examine how dense representations can improve the first-stage retrieval recall of passages for fact-checking in a noisy setting, and make the retrieval of relevant evidence more tractable on a large scale. however, the selection of relevant evidence sentences for accurate fact-checking and explainability remains a challenge. figure 1 shows an example of a claim and the retrieved passage which has three sentences, of which only the last sentence provides the critical evidence to refute the claim. we propose two ways to select the relevant sentences, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. we show that the former generalizes better with a high recall, while the latter has higher precision, making them suitable for the identification of relevant evidence sentences. our fact-checking system quin+ is able to verify open-domain claims using a large corpus or web search results. ## related work automated claim verification using a large corpus has not been studied systematically until the availability of the fact extraction and verification dataset (fever) @xcite . this dataset contains claims that are supported or refuted by specific evidence from wikipedia articles. prior to the work in @xcite , fact-checking solutions have relied on sparse passage retrieval, followed by a claim verification (entailment classification) model @xcite . other approaches used the mentions of entities in a claim and/or basic entity linking to retrieve documents and a machine learning model such as logistic regression or an enhanced sequential inference model to decide whether an article most likely contains the evidence @xcite @xcite . however, retrieval based on sparse representations and exact keyword matching can be rather restrictive for various queries. this restriction can be mitigated by dense representations using bertbased language models @xcite . the works in @xcite @xcite have successfully used such models and its variants for passage retrieval in open-domain question answering. the results can be further improved using passage re-ranking with cross-attention bert-based models @xcite . the work in @xcite is the first to propose a dense model to retrieve passages for fact-checking. apart from passage retrieval, sentence selection is also a critical task in fact-checking. these evidence sentences provide an explanation why a claim has been assessed to be credible or not. re-cent works have proposed a bert-based model for extracting relevant evidence sentences from multi-sentence passages @xcite . the authors observe that joint training on veracity prediction and explanation generation performs better than training separate models. the work in @xcite investigates how the few-shot learning capabilities of the gpt-3 model @xcite ) can be used for generating fact-checking explanations. ## the quin+ system the automated claim verification task can be defined as follows: given a textual claim c and a corpus @xmath0, d 2 , ..., d n }, where every passage d is comprised of sentences s j , 1 ≤ @xmath1, a system will return a set of evidence sentences ŝ ⊂ d i and a label ŷ ∈ {probably true, probably false, inconclusive}. we have developed an automated fact-checking system, called quin+, that verifies a given claim in three stages: passage retrieval from a corpus, sentence selection and entailment classification as shown in figure 2 . the label is determined as follows: we first perform entailment classification on the set of evidence sentences. when the number of retrieved evidence sentences that entail or contradict the claim is low, we label the claim as "inconclusive". if the number of evidence sentences that support the claim exceeds the number of sentences that refute the claim, we assign the label "probably true". otherwise, we assign the label "probably false". ## performance of quin+ we evaluate the three individual components of quin+ (retrieval, sentence selection and entailment classification) and finally perform an end-toend evaluation using various configurations. on the other hand, for the scifact dataset, we see that sequence labeling method remains the top performer in terms of precision and f1 score after fine-tuning, although its recall is lower than the embedding-based method. this shows that sequence labeling model is able to mitigate the high false positive rate observed with the embeddingbased selection method by taking into account the surrounding context. the factual-nli+ dataset contains claims with passages that either support or refute the claims with some sentences highlighted as ground truth specific evidence. finally, we carry out an end-to-end evaluation of our fact-checking system on factual-nli+ using various configurations of top-k passage retrieval (bm25, dense, hybrid, for various values of @xmath2, 100]) and evidence selection approaches (embdedding-based and sequence labeling). ## system demonstration we have created a demo for verifying opendomain claims using the top 20 results from a web search engine. for a given claim, quin+ returns relevant text passages with highlighted sentences. the passages are grouped into two sets, supporting and refuting. it computes a veracity rating based on the number of supporting and refuting evidence. it returns "probably true" if there are more supporting evidence, otherwise it returns "probably false". when the number of retrieved evidence is low, it returns "inconclusive". figure 4 shows a screen dump of the system with a claim that has been assessed to be probably false based on the overwhelming number of refuting sentence evidence (21 refute versus 0 support). quin+ can also be used on a large-scale corpus. ## conclusion & future work in this work, we have presented a three-stage factchecking system. we have demonstrated how a dense retrieval model can lead to higher recall when retrieving passages for fact-checking. we have also proposed two schemes to select relevant sentences: an embedding-based approach and a sequence labeling model to improve the claim verification accuracy. quin+ gave promising results in our extended factual-nli+ corpus, and is also able to verify open-domain claims using web search results. the source code of our system is publicly available foot_2 . even though our system is able to verify multiple open-domain claims successfully, it has some limitations. quin+ is not able to effectively verify multi-hop claims that require the retrieval of multiple pieces of evidence. for the verification of multi-hop claims, methodologies inspired by multi-hop question answering could be utilized. for the future development of large-scale factchecking systems we believe that a new benchmark needs to be introduced. the currently available datasets, including factual-nli+, are not suitable for evaluating the verification of claims using multiple sources.