{"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/what-do-questions-exactly-ask-mfae-duplicate","title":"What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis","arxiv_id":null,"date":"2020-05-07","proceeding":"SIAM International Conference on Data Mining (SDM20) 2020 5","authors":["Rong Zhang","Qifei Zhou","Bo Wu","Weiping Li","Tong Mo"],"abstract":"Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired questions is still challenging. Most previous works focus on the word-level or phrase-level semantic differences. We firstly propose to explore the asking emphasis of a question as a key factor in DQI. Asking emphasis bridges semantic equivalence between two questions. In this paper, we propose an attention model with multi-fusion asking emphasis (MFAE) for DQI. At first, BERT is used to obtain the dynamic pre-trained word embeddings. Then we get inter- and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is. Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation. Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data. In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets. The code is available at https://github.com/rzhangpku/MFAE.","url_abs":"https://epubs.siam.org/doi/10.1137/1.9781611976236.26","url_pdf":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.26","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":"what-do-questions-exactly-ask-mfae-duplicate","repo_url":"https://github.com/rzhangpku/MFAE","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"community-question-answering","task_name":"Community Question Answering"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"paraphrase-identification","task_name":"Paraphrase Identification"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/community-question-answering-on-quora","task":"Community Question Answering","dataset":"Quora Question Pairs","model":"MFAE","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"90.54"},"uses_additional_data":false},{"leaderboard":"/sota/natural-language-inference-on-multinli","task":"Natural Language Inference","dataset":"MultiNLI","model":"MFAE","rank_in_archive_order":39,"of":67,"metrics":{"Matched":"82.31","Mismatched":"81.43"},"uses_additional_data":false},{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"MFAE","rank_in_archive_order":14,"of":98,"metrics":{"% Test Accuracy":"90.07","% Train Accuracy":"93.18"},"uses_additional_data":false},{"leaderboard":"/sota/paraphrase-identification-on-quora-question","task":"Paraphrase Identification","dataset":"Quora Question Pairs","model":"MFAE","rank_in_archive_order":17,"of":31,"metrics":{"Accuracy":"90.54"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}