tasks.In this paper, we investigate whether graph structure is necessary for multi-hop reasoning tasks and what role it plays. Our analysis is centered on HotpotQA. We use the state-of-the-art published model, Dynamically Fused Graph Network (DFGN), as our baseline. By directly modifying the pre-trained model, our baseline model gains a large improvement and significantly surpass both published and unpublished works. Ablation experiments established that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop reasoning. We point out that both the graph structure and the 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, and achieve similar results to the previous state-of-the-art model achieved.
Recently, many works attempt to model texts as graph structure and introduce graph neural networks to deal with it on many NLP