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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2012.14500 |
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| _version_ | 1866912377876774912 |
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| author | Li, Xiangci Burns, Gully Peng, Nanyun |
| author_facet | Li, Xiangci Burns, Gully Peng, Nanyun |
| contents | Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_14500 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification Li, Xiangci Burns, Gully Peng, Nanyun Computation and Language Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction. |
| title | A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2012.14500 |