research-article

From Question to Text: Question-Oriented Feature Attention for Answer Selection

Authors:

Xin-Shun XuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 1

Article No.: 6, Pages 1 - 33

https://doi.org/10.1145/3233771

Published: 30 October 2018 Publication History

Abstract

Understanding unstructured texts is an essential skill for human beings as it enables knowledge acquisition. Although understanding unstructured texts is easy for we human beings with good education, it is a great challenge for machines. Recently, with the rapid development of artificial intelligence techniques, researchers put efforts to teach machines to understand texts and justify the educated machines by letting them solve the questions upon the given unstructured texts, inspired by the reading comprehension test as we humans do. However, feature effectiveness with respect to different questions significantly hinders the performance of answer selection, because different questions may focus on various aspects of the given text and answer candidates. To solve this problem, we propose a question-oriented feature attention (QFA) mechanism, which learns to weight different engineering features according to the given question, so that important features with respect to the specific question is emphasized accordingly. Experiments on MCTest dataset have well-validated the effectiveness of the proposed method. Additionally, the proposed QFA is applicable to various IR tasks, such as question answering and answer selection. We have verified the applicability on a crawled community-based question-answering dataset.

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Index Terms

From Question to Text: Question-Oriented Feature Attention for Answer Selection
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 1

January 2019

435 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3289475

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Publication History

Published: 30 October 2018

Accepted: 01 June 2018

Revised: 01 March 2018

Received: 01 July 2017

Published in TOIS Volume 37, Issue 1

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National Basic Research Program of China 973 Program
Project of Thousand Youth Talents 2016
National High Technology Research and Development Program of China 863 Program
Tencent AI Lab Rhino-Bird Joint Research Program
National Natural Science Foundation of China

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