Neural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in
the corpus all the time. To address this problem, we designed an iterative training process
and ensemble method based on boosting. We combined our method with different training
and decoding paradigms as the base model, including mutual-information-based decoding
and reward-augmented maximum likelihood learning. Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.
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This content will become publicly available on June 16, 2025
EDC: Effective and Efficient Dialog Comprehension for Dialog State Tracking
- Award ID(s):
- 2229876
- PAR ID:
- 10524876
- Publisher / Repository:
- In Proceedings of findings of 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024, findings)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Mexico City, Mexico
- Sponsoring Org:
- National Science Foundation
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