{"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/semi-supervised-question-retrieval-with-gated","title":"Semi-supervised Question Retrieval with Gated Convolutions","arxiv_id":"1512.05726","date":"2015-12-17","proceeding":"NAACL 2016 6","authors":["Tao Lei","Hrishikesh Joshi","Regina Barzilay","Tommi Jaakkola","Katerina Tymoshenko","Alessandro Moschitti","Lluis Marquez"],"abstract":"Question answering forums are rapidly growing in size with no effective\nautomated ability to refer to and reuse answers already available for previous\nposted questions. In this paper, we develop a methodology for finding\nsemantically related questions. The task is difficult since 1) key pieces of\ninformation are often buried in extraneous details in the question body and 2)\navailable annotations on similar questions are scarce and fragmented. We design\na recurrent and convolutional model (gated convolution) to effectively map\nquestions to their semantic representations. The models are pre-trained within\nan encoder-decoder framework (from body to title) on the basis of the entire\nraw corpus, and fine-tuned discriminatively from limited annotations. Our\nevaluation demonstrates that our model yields substantial gains over a standard\nIR baseline and various neural network architectures (including CNNs, LSTMs and\nGRUs).","url_abs":"http://arxiv.org/abs/1512.05726v2","url_pdf":"http://arxiv.org/pdf/1512.05726v2.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":"semi-supervised-question-retrieval-with-gated","repo_url":"https://github.com/taolei87/rcnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.05726","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}