@inproceedings{roller-etal-2021-recipes,
title = "Recipes for Building an Open-Domain Chatbot",
author = "Roller, Stephen and
Dinan, Emily and
Goyal, Naman and
Ju, Da and
Williamson, Mary and
Liu, Yinhan and
Xu, Jing and
Ott, Myle and
Smith, Eric Michael and
Boureau, Y-Lan and
Weston, Jason",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.24",
doi = "10.18653/v1/2021.eacl-main.24",
pages = "300--325",
abstract = "Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we highlight other ingredients. Good conversation requires blended skills: providing engaging talking points, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.",
}
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<abstract>Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we highlight other ingredients. Good conversation requires blended skills: providing engaging talking points, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.</abstract>
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%0 Conference Proceedings
%T Recipes for Building an Open-Domain Chatbot
%A Roller, Stephen
%A Dinan, Emily
%A Goyal, Naman
%A Ju, Da
%A Williamson, Mary
%A Liu, Yinhan
%A Xu, Jing
%A Ott, Myle
%A Smith, Eric Michael
%A Boureau, Y-Lan
%A Weston, Jason
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F roller-etal-2021-recipes
%X Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we highlight other ingredients. Good conversation requires blended skills: providing engaging talking points, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
%R 10.18653/v1/2021.eacl-main.24
%U https://aclanthology.org/2021.eacl-main.24
%U https://doi.org/10.18653/v1/2021.eacl-main.24
%P 300-325
Markdown (Informal)
[Recipes for Building an Open-Domain Chatbot](https://aclanthology.org/2021.eacl-main.24) (Roller et al., EACL 2021)
ACL
- Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, and Jason Weston. 2021. Recipes for Building an Open-Domain Chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 300–325, Online. Association for Computational Linguistics.