DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty, Xuezhe Ma, Aram Galstyan, and Nanyun Peng, 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Demonstrations Track, 2021.
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Abstract
Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present DiSCoL (Dialogue Systems through Coversational Line guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly convlines) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL’s pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to control the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
In our demo system called DiSCoL (https://t.co/32JAfKtvmH), we presented an engaging conversational system that uses conversational lines to guide the response generation. Users also have the control to change the dialog toward their more favorite direction.
— Sarik (@Sarikgha) March 19, 2021
Bib Entry
@article{ghazarian2021discol,
title = {DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation},
author = {Ghazarian, Sarik and Liu, Zixi and Chakrabarty, Tuhin and Ma, Xuezhe and Galstyan, Aram and Peng, Nanyun},
booktitle = {2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Demonstrations Track},
presentation_id = {https://underline.io/events/122/posters/4227/poster/20579-discol-toward-engaging-dialogue-systems-through-conversational-line-guided-response-generation},
pages = {26–34},
publisher = {Association for Computational Linguistics},
year = {2021}
}
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DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty, Xuezhe Ma, Aram Galstyan, and Nanyun Peng, 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Demonstrations Track, 2021.
Full Text Code Abstract BibTeX DetailsDetailsHaving engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present DiSCoL (Dialogue Systems through Coversational Line guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly convlines) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL’s pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to control the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
@article{ghazarian2021discol, title = {DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation}, author = {Ghazarian, Sarik and Liu, Zixi and Chakrabarty, Tuhin and Ma, Xuezhe and Galstyan, Aram and Peng, Nanyun}, booktitle = {2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Demonstrations Track}, presentation_id = {https://underline.io/events/122/posters/4227/poster/20579-discol-toward-engaging-dialogue-systems-through-conversational-line-guided-response-generation}, pages = {26–34}, publisher = {Association for Computational Linguistics}, year = {2021} }