Effective shared representations with multitask learning for community question answering

Daniele Bonadiman, Antonio Uva, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineer features. Unfortunately, DNNs usually require a lot of training data, especially for high-level semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20% relative improvement over standard DNNs.

Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages726-732
Number of pages7
Volume2
ISBN (Electronic)9781510838604
Publication statusPublished - 1 Jan 2017
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Other

Other15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
CountrySpain
CityValencia
Period3/4/177/4/17

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ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

Cite this

Bonadiman, D., Uva, A., & Moschitti, A. (2017). Effective shared representations with multitask learning for community question answering. In Short Papers (Vol. 2, pp. 726-732). Association for Computational Linguistics (ACL).