Year: 2019
Type: conference-paper
Abstract: Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs.We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models.Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as humanauthored sub-questions.We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance.Our experiments on HOTPOTQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.
DOI: 10.18653/v1/p19-1613
Authors: Sewon Min, Victor Zhong, Luke Zettlemoyer, Hannaneh Hajishirzi
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1390332038, G1717380892, G3812534482, RI: Small: Learning to Read, Ground, and Reason in Multimodal Text, CAREER: Learning Scalable Models for Grounded Semantic Parsing, G657448715, G7452299184, RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data, G8876996369, G8929337198
OpenAlex ID: W2949134692