On the benefit of incorporating external features in a neural architecture for answer sentence selection

Ruey Cheng Chen, Evi Yulianti, Mark Sanderson, W. Bruce Croft

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Incorporating conventional, unsupervised features into a neural architecture has the potential to improve modeling effectiveness, but this aspect is otten overlooked in the research of deep learning models for information retrieval. We investigate this incorporation in the context of answer sentence selection, and show that combining a set of query matching, readability, and query focus features into a simple convolutional neural network can lead to markedly increased effectiveness. Our results on two standard question-Answering datasets show the effectiveness of the combined model.

Original languageEnglish
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1017-1020
Number of pages4
ISBN (Electronic)9781450350228
DOIs
Publication statusPublished - 7 Aug 2017
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: 7 Aug 201711 Aug 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Country/TerritoryJapan
CityTokyo, Shinjuku
Period7/08/1711/08/17

Keywords

  • Answer sentence selection
  • Convolutional neural networks
  • External features

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