Effective FAQ Retrieval and Question Matching Tasks with Unsupervised Knowledge Injection

Wen Ting Tseng*, Yung Chang Hsu, Berlin Chen

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

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.

原文英語
主出版物標題Text, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings
編輯Kamil Ekštein, František Pártl, Miloslav Konopík
發行者Springer Science and Business Media Deutschland GmbH
頁面124-134
頁數11
ISBN(列印)9783030835262
DOIs
出版狀態已發佈 - 2021
事件24th International Conference on Text, Speech, and Dialogue, TSD 2021 - Olomouc, 捷克共和国
持續時間: 2021 九月 62021 九月 9

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12848 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

會議

會議24th International Conference on Text, Speech, and Dialogue, TSD 2021
國家/地區捷克共和国
城市Olomouc
期間2021/09/062021/09/09

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

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