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Asymmetry Sensitive Architecture for Neural Text Matching

Belkacem, Thiziri and Moreno, José G. and Dkaki, Taoufiq and Boughanem, Mohand Asymmetry Sensitive Architecture for Neural Text Matching. (2019) In: 41st European Conference on Information Retrieval (ECIR 2019), 14 April 2019 - 18 April 2019 (Cologne, Germany).

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Official URL: https://doi.org/10.1007/978-3-030-15719-7_8

Abstract

Question-answer matching can be viewed as a puzzle where missing pieces of information are provided by the answer. To solve this puzzle, one must understand the question to find out a correct answer. Semantic-based matching models rely mainly in semantic relatedness the input text words. We show that beyond the semantic similarities, matching models must focus on the most important words to find the correct answer. We use attention-based models to take into account the word saliency and propose an asymmetric architecture that focuses on the most important words of the question or the possible answers. We extended several state-of-the-art models with an attention-based layer. Experimental results, carried out on two QA datasets, show that our asymmetric architecture improves the performances of well-known neural matching algorithms.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in volume 11438 of Lecture Notes in Computer Science ISSN : 0302-9743 ISBN 978-3-030-15718-0 The original PDF is available at: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_8
HAL Id:hal-02435348
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited On:12 Dec 2019 09:11

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