{"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/response-selection-with-topic-clues-for","title":"Response Selection with Topic Clues for Retrieval-based Chatbots","arxiv_id":"1605.00090","date":"2016-04-30","proceeding":null,"authors":["Yu Wu","Wei Wu","Zhoujun Li","Ming Zhou"],"abstract":"We consider incorporating topic information into message-response matching to\nboost responses with rich content in retrieval-based chatbots. To this end, we\npropose a topic-aware convolutional neural tensor network (TACNTN). In TACNTN,\nmatching between a message and a response is not only conducted between a\nmessage vector and a response vector generated by convolutional neural\nnetworks, but also leverages extra topic information encoded in two topic\nvectors. The two topic vectors are linear combinations of topic words of the\nmessage and the response respectively, where the topic words are obtained from\na pre-trained LDA model and their weights are determined by themselves as well\nas the message vector and the response vector. The message vector, the response\nvector, and the two topic vectors are fed to neural tensors to calculate a\nmatching score. Empirical study on a public data set and a human annotated data\nset shows that TACNTN can significantly outperform state-of-the-art methods for\nmessage-response matching.","url_abs":"http://arxiv.org/abs/1605.00090v3","url_pdf":"http://arxiv.org/pdf/1605.00090v3.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":"response-selection-with-topic-clues-for","repo_url":"https://github.com/MarkWuNLP/TACNTN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}