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    <abstract>With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.</abstract>
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%0 Conference Proceedings
%T An End-to-End Multi-task Learning Model for Fact Checking
%A Li, Sizhen
%A Zhao, Shuai
%A Cheng, Bo
%A Yang, Hao
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-end
%X With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.
%R 10.18653/v1/W18-5523
%U https://aclanthology.org/W18-5523
%U https://doi.org/10.18653/v1/W18-5523
%P 138-144

Markdown (Informal)

[An End-to-End Multi-task Learning Model for Fact Checking](https://aclanthology.org/W18-5523) (Li et al., EMNLP 2018)

• An End-to-End Multi-task Learning Model for Fact Checking (Li et al., EMNLP 2018)

ACL

• Sizhen Li, Shuai Zhao, Bo Cheng, and Hao Yang. 2018. An End-to-End Multi-task Learning Model for Fact Checking. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 138–144, Brussels, Belgium. Association for Computational Linguistics.