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Characterizing Question Facets for Complex Answer Retrieval

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Authors: Sean MacAvaney, Andrew Yates, Arman Cohan, Luca Soldaini, Kai Hui, Nazli Goharian, Ophir Frieder

Appeared in: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018)

Links/IDs:
DOI 10.1145/3209978.3210135 DBLP conf/sigir/MacAvaneyYCSHGF18 ACM 3209978.3210135 arXiv 1805.00791 Google Scholar 7wWfoDgAAAAJ:IjCSPb-OGe4C Semantic Scholar 6addf7afa7f26204df05638b60ff1f5905d0fe88 smac.pub sigir2018-car

Abstract:

Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as 'History') to topical (facets that are specific to the question's topic, such as the 'Westward expansion' of the United States). We first explore a way to incorporate facet utility into ranking models during query term score combination. We then explore a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. When we use our techniques with a leading neural ranker on the TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and yield up to 26% higher performance than the next best method.

BibTeX @inproceedings{macavaney:sigir2018-car, author = {MacAvaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir}, title = {Characterizing Question Facets for Complex Answer Retrieval}, booktitle = {Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval}, year = {2018}, url = {https://arxiv.org/abs/1805.00791}, doi = {10.1145/3209978.3210135}, pages = {1205--1208} }