Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

By Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Hong Wang, Shiyu Chang, Murray Campbell, William Yang Wang. 2019

11 citations recorded by the source corpus.

A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve answer passages, i.e., the passages containing the groundtruth answers. However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question. To resolve this issue, we introduce a new subproblem of open-domain multi-hop QA, which aims to recognize the bridge (i.e., the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model. This model, the bridge reasoner, is trained with a weakly supervised signal and produces the candidate answer passages for the passage reader to extract the answer. On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1. Without using any memory inefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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

In Computer Vision and Pattern Recognition

In Information Retrieval & Search

In Natural Language Processing

Citations in the corpus

How Context Affects Language Models' Factual Predictions (2020, background): “…enhancing retrieval quality [Choi et al., 2017, Wang et al., 2018b,c, Lin et al., 2018, Min et al., 2018, Lee et al., 2018, 2019, Das et al., 2019, Xiong et al., 2019], improving answer aggregation [Clark and Gardner, 2018, Wang et al., 2018c, Lee et al., 2018, Pang et al., 2019], and…”
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering (2019, methodology): “Other graph retrieval methods have been developed, either using entity name matching (Ding et al., 2019; Xiong et al., 2019b; Godbole et al., 2019) or hyperlinks (Asai et al.”
Hierarchical Graph Network for Multi-hop Question Answering (2019, methodology): “Unpublished work includes Graph-based Recurrent Retriever (Anonymous, 2020b), MIR+EPS+BERT, Transformer-XH (Anonymous, 2020c), PR-BERT, and TPReasoner (Xiong et al., 2019).”