Effective FAQ Retrieval and Question Matching Tasks with Unsupervised Knowledge Injection

Wen Ting Tseng*, Yung Chang Hsu, Berlin Chen

*Corresponding author for this work

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

Abstract

Frequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas like e-commerce services, online forums and many others, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user’s query that is likely to reoccur frequently. To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure. In this paper, we extend this line of research by combining the clues gathered from the q-Q similarity measure and the q-A relevance measure, and meanwhile injecting extra word interaction information, distilled from a generic (open-domain) knowledge base, into a contextual language model for inferring the q-A relevance. Furthermore, we also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner, acting as a surrogate to the supervised domain-specific knowledge base information. As such, it enables the model to equip sentence representations with the knowledge about domain-specific and topically-relevant relations among words, thereby providing a better q-A relevance measure. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset (viz. TaipeiQA), and further apply and contextualize it to a large-scale question-matching task (viz. LCQMC), which aims to search questions from a QA dataset that have a similar intent as an input query. Extensive experimental results on these two datasets confirm the promising performance of the proposed approach in relation to some state-of-the-art ones.

Original languageEnglish
Title of host publicationText, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings
EditorsKamil Ekštein, František Pártl, Miloslav Konopík
PublisherSpringer Science and Business Media Deutschland GmbH
Pages124-134
Number of pages11
ISBN (Print)9783030835262
DOIs
Publication statusPublished - 2021
Event24th International Conference on Text, Speech, and Dialogue, TSD 2021 - Olomouc, Czech Republic
Duration: 2021 Sep 62021 Sep 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12848 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Text, Speech, and Dialogue, TSD 2021
Country/TerritoryCzech Republic
CityOlomouc
Period2021/09/062021/09/09

Keywords

  • Frequently asked question
  • Knowledge graph
  • Language model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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