{"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/incorporating-label-dependency-for-answer","title":"Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF","arxiv_id":null,"date":"2016-12-01","proceeding":"COLING 2016 12","authors":["Yang Xiang","Xiaoqiang Zhou","Qingcai Chen","Zhihui Zheng","Buzhou Tang","Xiaolong Wang","Yang Qin"],"abstract":"In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Net-works, Long Short Term Memory and Conditional Random Fields. Extensive experi-ments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96{\\%} on macro averaged F1, which improves the state-of-the-art neural network-based method by 2.82{\\%} and outper-forms the Top-1 system in the shared task by 1.77{\\%}. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29{\\%} on overall F1 and gains the best performance on the Good and Bad categories.","url_abs":"https://aclanthology.org/C16-1117","url_pdf":"https://aclanthology.org/C16-1117.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":"incorporating-label-dependency-for-answer","repo_url":"https://github.com/o0laika0o/CNN-LSTM-CRF-for-cQA-answer-tagging","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-question-answering","task_name":"Community Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}