MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation

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MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation
Title:
MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation
Journal Title:
Proceedings of the AAAI Conference on Artificial Intelligence
Keywords:
Publication Date:
28 June 2022
Citation:
Zhang, C., D’Haro, L. F., Friedrichs, T., & Li, H. (2022). MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11657–11666. https://doi.org/10.1609/aaai.v36i10.21420
Abstract:
Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure the quality of such conversational agents, a dialogue evaluator is expected to conduct assessment across domains as well. However, most of the state-of-the-art automatic dialogue evaluation metrics (ADMs) are not designed for multi-domain evaluation. We are motivated to design a general and robust framework, MDD-Eval, to address the problem. Specifically, we first train a teacher evaluator with human-annotated data to acquire a rating skill to tell good dialogue responses from bad ones in a particular domain and then, adopt a self-training strategy to train a new evaluator with teacher-annotated multi-domain data, that helps the new evaluator to generalize across multiple domains. MDD-Eval is extensively assessed on six dialogue evaluation benchmarks. Empirical results show that the MDD-Eval framework achieves a strong performance with an absolute improvement of 7% over the state-of-the-art ADMs in terms of mean Spearman correlation scores across all the evaluation benchmarks.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - National Robotics Program
Grant Reference no. : 1922500054

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering (AME) Programmatic Funding Scheme
Grant Reference no. : A18A2b0046

This research / project is supported by the Robert Bosch (SEA) Pte Ltd - EDB’s Industrial Postgraduate Programme – II (EDB-IPP), project title: Applied Natural Language Processing
Grant Reference no. :
Description:
© 2022, Association for the Advancement of Artificial Intelligence. This material may not be retransmitted or redistributed without permission in writing from The Association for the Advancement of Artificial Intelligence. Permission to use document is granted, provided that (1) the copyright notice appears in all copies and that both the copyright notice and this permission notice appear, (2) use of such documents is for personal use only, and will not be copied or posted on any network computer or broadcast in any media, and (3) no modifications of any documents are made.
ISSN:
2374-3468
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