@inproceedings{shao-etal-2020-graph,
title = "Is {G}raph {S}tructure {N}ecessary for {M}ulti-hop {Q}uestion {A}nswering?",
author = "Shao, Nan and
Cui, Yiming and
Liu, Ting and
Wang, Shijin and
Hu, Guoping",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.583",
doi = "10.18653/v1/2020.emnlp-main.583",
pages = "7187--7192",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Is Graph Structure Necessary for Multi-hop Question Answering?
%A Shao, Nan
%A Cui, Yiming
%A Liu, Ting
%A Wang, Shijin
%A Hu, Guoping
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F shao-etal-2020-graph
%X 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.
%R 10.18653/v1/2020.emnlp-main.583
%U https://aclanthology.org/2020.emnlp-main.583
%U https://doi.org/10.18653/v1/2020.emnlp-main.583
%P 7187-7192
Markdown (Informal)
[Is Graph Structure Necessary for Multi-hop Question Answering?](https://aclanthology.org/2020.emnlp-main.583) (Shao et al., EMNLP 2020)
ACL