Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification (2021)

Abstract: Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction. Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules. We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.

## introduction a system of scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. to address this issue, @xcite introduced scientific claim verification which consists of three tasks. as illustrated in figure 1 , for a given claim, the system finds the abstracts which are related to the claim from a scholarly document corpus (abstract retrieval task); it selects the sentences which are the evidences in the abstract related to the claim (rationale selection task); it also classifies whether the abstract/sentences support or refute the claims (stance prediction task). @xcite also provided a dataset called scifact. most of the existing works of general claim verification are based on pipeline models @xcite @xcite , 2020; @xcite @xcite ; some works utilize joint optimization strategies @xcite @xcite . these models attempted to jointly optimize the rationale selection and stance prediction, but did not directly link the two modules @xcite . in the case of the scientific claim verification, @xcite proposed a baseline model verisci based on a pipeline of three components for the three tasks. @xcite proposed a pipeline model called vert5erini which utilized the pre-trained language model t5 @xcite and adapted a pre-trained sequence-to-sequence model. @xcite jointly trained two tasks of rationale selection and stance prediction, and had a pipeline on abstract retrieval task and the joint module. above existing works on scientific claim verification are fully or partially pipeline solutions. one problem of these works is the error propagation among the modules in the pipeline. another problem is that the module in the pipeline trained independently cannot share and leverage the valuable information among each other. therefore, we propose an approach, named as arsjoint, which jointly learns the three modules for the three tasks. it has a machine reading comprehension (mrc) framework which uses the claim content as the query to learn additional information. in addition, we assume that the abstract retrieval module should have good interpretability and tend to assign high sentence-level attention scores to the evidence sentences that influence the retrieval results; it is consistent with the goal of the rationale selection module. we thus enhance the information exchanges and constraints among tasks by proposing a regularization term based on a symmetric divergence to bridge these two modules. the experimental results on the benchmark dataset scifact show that the proposed approach has better performance than the existing works. the main contributions of this paper can be summarized as follows. (1). we propose a scientific claim verification approach which jointly trains on the three tasks in a mrc framework. (2). we propose a regularization based on the divergence between the sentence attention of abstract retrieval and the outputs of rational selection. ## conclusion in this paper, we propose a joint model named as arsjoint on three tasks of abstract retrieval, rationale selection and stance prediction for scientific claim verification in a mrc framework by including claim. we also propose a regularization based on the divergence between the sentence attention of the abstract retrieval task and the outputs of the rational selection task. the experimental results illustrate that our method achieves better results on the benchmark dataset scifact. in future work, we will try to pre-train the model on other general claim verification datasets such as fever @xcite to improve the performance.