A DQN -based Approach to Finding Precise Evidences for Fact Verification (2021)

Abstract: Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.

## introduction with the growing false information, such as fake news, political deception and online rumors, automatic fact-checking systems have emerged to automatically identify and filter this information. fact verification (fv) is a special fact-checking task that aims to retrieve related evidences from a text corpus to verify a textual claim. taking figure 1 as example, an existing method for fv first retrieves related documents from the given corpus at stage 1 (namely the document retrieval stage), then finds key sentences from the documents at stage 2 (namely the sentence selection stage), and finally treats the set of key sentences as an evidence to verify the claim at stage 3 (namely the claim verification stage). as can be seen in this example, it is desirable to retrieve an evidence consisting of the first two sentences only, since it does not contain unnecessary sentences to determine the truthfulness of the claim and can alleviate human efforts to further validate the evidence. more importantly, an evidence containing unnecessary sentences may involve conflicting pieces some of which support the claim while the other refute the claim. thus, it is crucial to compute minimal sets of sentences that can determine the truthfulness of the claim. in this paper, we refer to a minimal set of sentences that supports or refutes a given claim as a precise evidence. existing methods for fv do not target the retrieval of precise evidences. most existing studies @xcite @xcite @xcite @xcite formulate fv as a three-stage pipeline task as illustrated in figure 1 . this way makes the retrieval of precise evidences extremely difficult since the sentence selection stage is required to select a precise set of relevant sentences rather than a fixed number of sentences as in existing methods. to the best of our knowledge, twowin-gos @xcite is the only method by now which does not follow the three-stage pipeline. instead, it exploits a supervised training scheme to train the last two stages jointly and is able to compute precise evidences. however, it exhibits a significantly worse performance than other state-ofthe-art methods for fv, especially in terms of the recall of evidences. therefore, there is still a need for designing new methods to compute precise evidences. these methods are expected to achieve better performance than twowingos. it is challenging to compute precise evidences. on one hand, the search space for precise evidences is very large. for example, in the benchmark fact extraction and verification (fever) dataset @xcite the average number of sentences for each claim input to the sentence selection stage is 40, and an output evidence has up to 5 sentences. hence there are up to 5 @xmath0, 098 candidates in the search space. on the other hand, greedy search of precise evidences easily falls into a local optimum. as shown in our experiments (see inspired by the strong exploration ability of the deep q-learning network (dqn) @xcite , we develop a dqn-based approach to retrieval of precise evidences. in this approach, we first employ dqn to compute candidate pairs of precise evidences and their labels, and then use a post-processing strategy to refine candidate pairs. we notice that q-values computed by dqn has label bias due to two reasons. on one hand, the label "not enough info" does not locate at the same concept level as "supports" or "re-futes". on the other hand, there is not a fixed range for q-values, making q-values hard to accurately estimate. thus, a post-processing strategy is needed to tackle the label bias on q-values. we develop such a strategy to seek best thresholds in determining the true labels of computed evidences. our experimental results on fever @xcite confirm that our dqn-based approach is effective in finding precise evidences. more importantly, the approach is shown to outper-form state-of-the-art methods for fv. ## conclusion and future work in this paper, we have proposed a novel dqn-based approach to finding precise evidences for fact verification. it provides a method to solve the preciseevidence problem by first employing a dqn to compute some candidates and then introducing a post-processing strategy to extract the target evidence and its label from the candidates. experimental results show that the approach achieves state-of-the-art performance in terms of retrieval of precise evidences. besides, to the best of our knowledge, it is the first attempt to employ dqn in the fact verification task. future work will incorporate external knowledge into our approach to improve the retrieval recall.