@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}