Latent space embedding for retrieval in question-answer archives

Deepak, P and Garg, D and Shevade, S (2017) Latent space embedding for retrieval in question-answer archives. In: 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9 - 11 September 2017, Copenhagen, pp. 855-865.

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Abstract

Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-the-art methods.

Item Type:	Conference Paper
Publication:	EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
Publisher:	Association for Computational Linguistics (ACL)
Additional Information:	The copyright for this article belongs to Association for Computational Linguistics (ACL).
Keywords:	Deep learning; Semantics, Community-driven question answering; Empirical analysis; Local neighborhood structures; Question-answer pairs; Retrieval techniques; Semantic similarity; State-of-the-art methods; Translation models, Natural language processing systems
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	18 Jul 2022 04:48
Last Modified:	18 Jul 2022 04:48
URI:	https://eprints.iisc.ac.in/id/eprint/74625

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