Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

TL;DR

This paper introduces an unsupervised method for selecting justification sentences in multi-hop question answering, improving accuracy and stability without external training resources.

Contribution

The proposed unsupervised sentence selection approach enhances multi-hop QA performance and stability, achieving new state-of-the-art results on multiple datasets without external resources.

Findings

Improves QA performance on ARC and MultiRC datasets.

Outperforms retrieval baselines in justification quality.

More stable across different domains than supervised methods.

Abstract

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.