Abstract
There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.- Anthology ID:
- K18-1053
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 551–561
- Language:
- URL:
- https://aclanthology.org/K18-1053
- DOI:
- 10.18653/v1/K18-1053
- Cite (ACL):
- Yury Zemlyanskiy and Fei Sha. 2018. Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 551–561, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner (Zemlyanskiy & Sha, CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/K18-1053.pdf
- Data
- DailyDialog