Is Graph Structure Necessary for Multi-hop Question Answering?

By Nan Shao, Yiming Cui, Ting-Wei Liu, Shijin Wang, Guoping Hu. 2020

36 citations recorded by the source corpus.

Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we investigate whether the graph structure is necessary for textual multi-hop reasoning. Our analysis is centered on HotpotQA. We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for textual multi-hop reasoning. We point out that both graph structure and adjacency matrix are task-related prior knowledge, and graph-attention can be considered as a special case of self-attention. Experiments demonstrate that graph-attention or the entire graph structure can be replaced by self-attention or Transformers.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

How researchers see this paper

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

In Artificial Intelligence

1. Qian Liu
2. Jian-Guang Lou
3. Zeqi Lin
4. Qiang Fu
5. Wai Lam
6. Yang Deng
7. Xinyu Pi
8. Yan Gao

In Information Retrieval & Search

1. Wai Lam
2. Yang Deng
3. Deng Cai
4. Weizhu Chen

In Natural Language Processing

1. Qian Liu
2. Wai Lam
3. Yang Deng
4. Yan Gao
5. Deng Cai
6. Morteza Ziyadi
7. Weizhu Chen
8. Huihui Zhang

Citations in the corpus

• Reasoning Like Program Executors (2022, methodology): “, 2020), C2F Reader (Shao et al., 2020) and the SOTA model HGN (Fang et al.”
• Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021, methodology): “Comparing to named entity (Shao et al., 2020), we use AMR as our graph annotation because it is not specifically designed for a particular domain and can be adapted to a wide range of textual data.”