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        <title>Latent Space Embedding for Retrieval in Question-Answer Archives</title>
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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.</abstract>
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%0 Conference Proceedings
%T Latent Space Embedding for Retrieval in Question-Answer Archives
%A P, Deepak
%A Garg, Dinesh
%A Shevade, Shirish
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F p-etal-2017-latent
%X 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.
%R 10.18653/v1/D17-1089
%U https://aclanthology.org/D17-1089
%U https://doi.org/10.18653/v1/D17-1089
%P 855-865

Markdown (Informal)

[Latent Space Embedding for Retrieval in Question-Answer Archives](https://aclanthology.org/D17-1089) (P et al., EMNLP 2017)

• Latent Space Embedding for Retrieval in Question-Answer Archives (P et al., EMNLP 2017)

ACL

• Deepak P, Dinesh Garg, and Shirish Shevade. 2017. Latent Space Embedding for Retrieval in Question-Answer Archives. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 855–865, Copenhagen, Denmark. Association for Computational Linguistics.