Question-answering aspect classification with hierarchical attention network

Hanqian Wu, Mumu Liu, Jingjing Wang, Jue Xie, Chenlin Shen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

In e-commerce websites, user-generated question-answering text pairs generally contain rich aspect information of products. In this paper, we address a new task, namely Question-answering (QA) aspect classification, which aims to automatically classify the aspect category of a given QA text pair. In particular, we build a high-quality annotated corpus with specifically designed annotation guidelines for QA aspect classification. On this basis, we propose a hierarchical attention network to address the specific challenges in this new task in three stages. Specifically, we firstly segment both question text and answer text into sentences, and then construct (sentence, sentence) units for each QA text pair. Second, we leverage a QA matching attention layer to encode these (sentence, sentence) units in order to capture the aspect matching information between the sentence inside question text and the sentence inside answer text. Finally, we leverage a self-matching attention layer to capture different importance degrees of different (sentence, sentence) units in each QA text pair. Experimental results demonstrate that our proposed hierarchical attention network outperforms some strong baselines for QA aspect classification.

Original languageEnglish
Title of host publicationChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Subtitle of host publication17th China National Conference, CCL 2018 and 6th International Symposium, NLP-NABD 2018 Changsha, China, October 19–21, 2018 Proceedings
EditorsMaosong Sun, Ting Liu, Xiaojie Wang, Zhiyuan Liu, Yang Liu
Place of PublicationCham Switzerland
PublisherSpringer
Pages225-237
Number of pages13
ISBN (Electronic)9783030017163
ISBN (Print)9783030017156
DOIs
Publication statusPublished - 2018
EventChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data 2018
- Changsha, China
Duration: 19 Oct 201821 Oct 2018
Conference number: 17th & 6th
http://www.cips-cl.org/static/CCL2018/index.html

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11221
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data 2018
Abbreviated titleCCL 2018 & NLP-NABD 2018
CountryChina
CityChangsha
Period19/10/1821/10/18
Internet address

Keywords

  • Aspect classification
  • Hierarchical attention
  • Question answering

Cite this

Wu, H., Liu, M., Wang, J., Xie, J., & Shen, C. (2018). Question-answering aspect classification with hierarchical attention network. In M. Sun, T. Liu, X. Wang, Z. Liu, & Y. Liu (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: 17th China National Conference, CCL 2018 and 6th International Symposium, NLP-NABD 2018 Changsha, China, October 19–21, 2018 Proceedings (pp. 225-237). (Lecture Notes in Computer Science ; Vol. 11221 ). Springer. https://doi.org/10.1007/978-3-030-01716-3_19