{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lstm-based-deep-learning-models-for-non","title":"LSTM-based Deep Learning Models for Non-factoid Answer Selection","arxiv_id":"1511.04108","date":"2015-11-12","proceeding":null,"authors":["Ming Tan","Cicero Dos Santos","Bing Xiang","Bo-Wen Zhou"],"abstract":"In this paper, we apply a general deep learning (DL) framework for the answer\nselection task, which does not depend on manually defined features or\nlinguistic tools. The basic framework is to build the embeddings of questions\nand answers based on bidirectional long short-term memory (biLSTM) models, and\nmeasure their closeness by cosine similarity. We further extend this basic\nmodel in two directions. One direction is to define a more composite\nrepresentation for questions and answers by combining convolutional neural\nnetwork with the basic framework. The other direction is to utilize a simple\nbut efficient attention mechanism in order to generate the answer\nrepresentation according to the question context. Several variations of models\nare provided. The models are examined by two datasets, including TREC-QA and\nInsuranceQA. Experimental results demonstrate that the proposed models\nsubstantially outperform several strong baselines.","url_abs":"http://arxiv.org/abs/1511.04108v4","url_pdf":"http://arxiv.org/pdf/1511.04108v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lstm-based-deep-learning-models-for-non","repo_url":"https://github.com/deepmipt/DeepPavlov","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"lstm-based-deep-learning-models-for-non","repo_url":"https://github.com/sachinbiradar9/question-answer-selection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"answer-selection","task_name":"Answer Selection"},{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.04108","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}