Name
Abstract | ||
|---|---|---|
Claim verification aims to verify the truthfulness of claims according to the evidence. In the real-world scenarios, the claim verification methods are required 1) to identify and filter out the noise; 2) to enable the interaction among evidence; and 3) to predict the labels in mixed label sets. However, the prior studies adopt either the implicit or cumbersome interaction processes, which disable them from capturing the commonalities among evidence nor reducing the impact of noise, and few of them consider the label prediction in mixed label sets. To alleviate these issues, we propose an Enhanced Dynamic Interaction Network (EDIN). The EDIN contains two main modules: A Gate-based Dynamic Interaction Module to promote the interaction of evidence and summarize the commonality to verify the claim, which employs hub node and gates to control the information transmission and denoise. An Enhancement Module to assist the EDIN with predicting labels in a mixed label sets environment, where we introduce the auxiliary domain detection task and label transition component to enhance the expressiveness of label representation. Experimental results on multiple public datasets reveal the superiority of our method, and further analysis validates the effectiveness of our proposed modules. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2020.12.112 | Neurocomputing |
Keywords | DocType | Volume |
Claim verification,Sentence interaction,Multi-task learning,Denoising | Journal | 439 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peiguang Li | 1 | 0 | 0.68 |
| Xian Sun | 2 | 16 | 5.49 |
| Hongfeng Yu | 3 | 0 | 3.72 |
| Wenkai Zhang | 4 | 0 | 4.73 |
| Guangluan Xu | 5 | 2 | 2.07 |