Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2020

Abstract

Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.

Keywords

answer helpfulness prediction, question answering, E-commerce

Discipline

Databases and Information Systems | E-Commerce

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

WWW '20: Proceedings of The Web Conference 2020, Taipei, Taiwan, April 20-24

First Page

2620

Last Page

2626

ISBN

9781450370233

Identifier

10.1145/3366423.3380015

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3366423.3380015

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