Abstract

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of which are initialized with pre-trained contextual encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multi-hop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction). By weaving heterogeneous nodes into an integral unified graph, this hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art, outperforming existing multi-hop QA approaches.

Highlights

  • In contrast to one-hop question answering (Rajpurkar et al, 2016; Trischler et al, 2016; Lai et al, 2017) where answers can be derived from a single paragraph (Wang and Jiang, 2017; Seo et al, 2017; Liu et al, 2018; Devlin et al, 2019), many recent studies on question answering focus on multi-hop reasoning across multiple documents or paragraphs

  • We propose a Hierarchical Graph Network (HGN) for multi-hop question answering, which empowers joint answer/evidence prediction via multi-level fine-grained graphs in a hierarchical framework

  • The main contributions of this paper are threefold: (i) We propose a Hierarchical Graph Network (HGN) for multi-hop question answering, where heterogeneous nodes are woven into an integral hierarchical graph. (ii) Nodes from different granularity levels mutually enhance each other for different sub-tasks, providing effective supervision signals for both supporting facts extraction and answer prediction. (iii) On the HotpotQA benchmark, the proposed model achieves new state of the art in both Distractor and Fullwiki settings

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Summary

Introduction

In contrast to one-hop question answering (Rajpurkar et al, 2016; Trischler et al, 2016; Lai et al, 2017) where answers can be derived from a single paragraph (Wang and Jiang, 2017; Seo et al, 2017; Liu et al, 2018; Devlin et al, 2019), many recent studies on question answering focus on multi-hop reasoning across multiple documents or paragraphs. Popular tasks include WikiHop (Welbl et al, 2018), ComplexWebQuestions (Talmor and Berant, 2018), and HotpotQA (Yang et al, 2018). In order to correctly answer the question (“The director of the romantic comedy ‘Big Stone Gap’ is based in what New York city”), the model is required to first identify P1 as a relevant paragraph, whose title contains the keywords that appear in the question (“Big Stone Gap”). S1, the first sentence of P1, is chosen by the model as a supporting fact that leads to the next-hop paragraph P2. From P2, the span “Greenwich Village, New York City” is selected as the predicted answer

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