{"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/a-dual-encoder-sequence-to-sequence-model-for","title":"A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling","arxiv_id":"1710.10520","date":"2017-10-28","proceeding":null,"authors":["Sharath T. S.","Shubhangi Tandon","Ryan Bauer"],"abstract":"Ever since the successful application of sequence to sequence learning for\nneural machine translation systems, interest has surged in its applicability\ntowards language generation in other problem domains. Recent work has\ninvestigated the use of these neural architectures towards modeling open-domain\nconversational dialogue, where it has been found that although these models are\ncapable of learning a good distributional language model, dialogue coherence is\nstill of concern. Unlike translation, conversation is much more a one-to-many\nmapping from utterance to a response, and it is even more pressing that the\nmodel be aware of the preceding flow of conversation. In this paper we propose\nto tackle this problem by introducing previous conversational context in terms\nof latent representations of dialogue acts over time. We inject the latent\ncontext representations into a sequence to sequence neural network in the form\nof dialog acts using a second encoder to enhance the quality and the coherence\nof the conversations generated. The main task of this research work is to show\nthat adding latent variables that capture discourse relations does indeed\nresult in more coherent responses when compared to conventional sequence to\nsequence models.","url_abs":"http://arxiv.org/abs/1710.10520v1","url_pdf":"http://arxiv.org/pdf/1710.10520v1.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":"a-dual-encoder-sequence-to-sequence-model-for","repo_url":"https://github.com/remilb/ghissu-bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}