Kernel Graph Attention Network for Fact Verification

By Zhenghao Liu, Chenyan Xiong, Maosong Sun. 2019

10 citations recorded by the source corpus.

Subfields: Natural Language Processing, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Zhiyuan Liu
2. Maosong Sun
3. Chenyan Xiong
4. Zhenghao Liu
5. Nan Duan
6. M. Zhou
7. Yankai Lin
8. Duyu Tang

In Graph Learning

1. Zhiyuan Liu
2. Maosong Sun
3. Yankai Lin
4. Chenyan Xiong
5. Zhenghao Liu
6. Duyu Tang
7. Nan Duan
8. Wanjun Zhong

Citations in the corpus

• Reasoning Over Semantic-Level Graph for Fact Checking (2019, methodology): “It is worth noting that, our approach, which exploits explicit graph-level semantic structure of evidence obtained by SRL, outperforms GEAR and KGAT, both of which regard sentences as the nodes and use model to learn the implicit structure of evidence 8 .”
• Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention (2020, methodology): “Our ablation studies demonstrate Transformer-XH’s efficacy on questions that are known to require multi-hop reasoning (Min et al., 2019b) and on verifying multi-evidence claims (Liu et al., 2019b).”
• WikiFactFind: Semi-automated fact-checking based on Wikipedia (2022, background): “However, even state-of-the-art fully automated solutions show the accuracy of about 75%, which is far from desired human-level performance [9].”
• Coreferential Reasoning Learning for Language Representation (2020, methodology): “Similar to previous language representation models (Devlin et al., 2019; Yang et al., 2019; Joshi et al., 2019a; Liu et al., 2019a), we adopt English Wikipeida 4 as our training corpus, which contains about 3,000M tokens.”