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Supporting Human Answers for Advice-Seeking Questions in CQA Sites

  • Conference paper
Book cover Advances in Information Retrieval (ECIR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9626))

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Abstract

In many questions in Community Question Answering sites users look for the advice or opinion of other users who might offer diverse perspectives on a topic at hand. The novel task we address is providing supportive evidence for human answers to such questions, which will potentially help the asker in choosing answers that fit her needs. We present a support retrieval model that ranks sentences from Wikipedia by their presumed support for a human answer. The model outperforms a state-of-the-art textual entailment system designed to infer factual claims from texts. An important aspect of the model is the integration of relevance oriented and support oriented features.

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Notes

  1. 1.

    All SDM scoring function components in Eq. 1 also use the logs of Dirichlet smoothed estimates [3]. The smoothing parameter, \(\mu \), is set to the same value for all estimates.

  2. 2.

    Smoothing is performed using the term statistics in the document corpus \(D\).

  3. 3.

    https://code.google.com/p/word2vec/.

  4. 4.

    http://nlp.stanford.edu/software/corenlp.shtml.

  5. 5.

    IMDB snapshot from 08/01/2014.

  6. 6.

    The order of concatenation has no effect since unigram language models are used.

  7. 7.

    http://nlp.stanford.edu/software/lex-parser.shtml.

  8. 8.

    Available at http://iew3.technion.ac.il/~kurland/supportRanking.

  9. 9.

    answers.yahoo.com.

  10. 10.

    www.crowdflower.com.

  11. 11.

    The implementations of LinearSVM and PolySVM are from http://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html. The LambdaMART implementation is from http://sourceforge.net/p/lemur/wiki/RankLib/. All methods are used with default free-parameter values of the corresponding implementations.

  12. 12.

    www.lemurproject.org.

  13. 13.

    http://hltfbk.github.io/Excitement-Open-Platform/.

  14. 14.

    We trained P1EDA using the SNLI data set [15], which contains 549,366 examples.

  15. 15.

    Integrating P1EDA in PolySVM did not yield support-ranking improvements.

  16. 16.

    For relevance ranking, LambdaMART was trained for binary relevance.

  17. 17.

    Actual numbers are omitted due to space considerations and as they convey no additional insight.

  18. 18.

    Ablation tests reveal that removing this feature results in the second most substantial decrease of support-ranking performance among all features.

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Acknowledgments

We thank the reviewers for their helpful comments, and Omer Levy and Vered Shwartz for their help with the textual entailment tool used for experiments. This work was supported in part by a Yahoo! faculty research and engagement award.

Author information

Authors and Affiliations

  1. Faculty of Industrial Engineering and Management, Technion, 32000, Haifa, Israel

    Liora Braunstain, Oren Kurland & Anna Shtok

  2. Yahoo Labs, 31905, Haifa, Israel

    David Carmel & Idan Szpektor

Authors
  1. Liora Braunstain
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  2. Oren Kurland
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  3. David Carmel
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  4. Idan Szpektor
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  5. Anna Shtok
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Corresponding author

Correspondence to Liora Braunstain .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padua, Padova, Italy

    Nicola Ferro

  2. Faculty of Informatics, University of Lugano (USI), Lugano, Switzerland

    Fabio Crestani

  3. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  4. Systèmes d’informations, Big Data et Recherche d’Information, Institut de Recherche en Informatique de Toulouse IRIT/équipe SIG, Toulouse Cedex 04, France

    Josiane Mothe

  5. Yahoo! Labs London, London, UK

    Fabrizio Silvestri

  6. Department of Information Engineering, University of Padua, Padova, Italy

    Giorgio Maria Di Nunzio

  7. TU Delft - EWI/ST/WIS, Delft, The Netherlands

    Claudia Hauff

  8. Department of Information Engineering, University of Padua, Padova, Italy

    Gianmaria Silvello

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© 2016 Springer International Publishing Switzerland

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Braunstain, L., Kurland, O., Carmel, D., Szpektor, I., Shtok, A. (2016). Supporting Human Answers for Advice-Seeking Questions in CQA Sites. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-30671-1_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30670-4

  • Online ISBN: 978-3-319-30671-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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