Latent Space Embedding for Retrieval in Question-Answer Archives

Deepak Padmanabhan, Dinesh Garg, Shirish Shevade

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

4 Citations (Scopus)
141 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the Conference on Empirical Methods in Natural Language Processing 2017
Publication statusPublished - 01 Sep 2017
EventEMNLP 2017: International Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark, Denmark
Duration: 07 Sep 201711 Sep 2017


ConferenceEMNLP 2017
Internet address


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