Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations

Karthik Gopalakrishnan, Behnam Hedayatnia, Qinlang Chen, Anna Gottardi, Sanjeev Kwatra, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tür


Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles for conversation partners. These datasets also do not explore depth or breadth of topical coverage with transitions in conversations. We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles, to help further research in open-domain conversational AI. We also train several state-of-the-art encoder-decoder conversational models on Topical-Chat and perform automated and human evaluation for benchmarking.


 DOI: 10.21437/Interspeech.2019-3079

Cite as: Gopalakrishnan, K., Hedayatnia, B., Chen, Q., Gottardi, A., Kwatra, S., Venkatesh, A., Gabriel, R., Hakkani-Tür, D. (2019) Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations. Proc. Interspeech 2019, 1891-1895, DOI: 10.21437/Interspeech.2019-3079.


@inproceedings{Gopalakrishnan2019,
  author={Karthik Gopalakrishnan and Behnam Hedayatnia and Qinlang Chen and Anna Gottardi and Sanjeev Kwatra and Anu Venkatesh and Raefer Gabriel and Dilek Hakkani-Tür},
  title={{Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={1891--1895},
  doi={10.21437/Interspeech.2019-3079},
  url={http://dx.doi.org/10.21437/Interspeech.2019-3079}
}