Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering
This addresses the need for reliable justification selection in multi-hop QA, offering a domain-stable, unsupervised approach that enhances existing supervised models, though it is incremental as it builds on prior QA methods.
The authors tackled the problem of selecting justification sentences for multi-hop question answering by proposing an unsupervised method that improves relevance, reduces overlap, and enhances coverage, resulting in new state-of-the-art performance on ARC (56.82% F1) and MultiRC (26.1% EM0) datasets without external resources.
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.