Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2021 | SDU@AAAI | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangci Li | 1 | 2 | 1.44 |
| Gully A. P. C. Burns | 2 | 172 | 12.17 |
| Nanyun Peng | 3 | 155 | 28.78 |