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Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
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Bing Liu, Gokhan Tür, Dilek Hakkani-Tür, Pararth Shah, Larry Heck. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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“Automated metrics like BLEU and ROUGE [99] often do not correlate with human judgment [103, 164, 166, 177], making them unreliable for evaluation and training.”
From RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs (2026) · cites this paper as [103]
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Metadata
- Authors
- Liu, B., Tür, G., Hakkani-Tür, D., Shah, P., & Heck, L.
- Affiliations
- Carnegie Mellon University (US)
- Google (United States) (US)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 2060-2069
- OpenAlex
- W2963567240
- Cited by
- 148
- References
- 40
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 7 · 2023: 17 · 2022: 26 · 2021: 29 · 2020: 32 · 2019: 20 · 2018: 6
- FWCI
- 8.88
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- human teaching, human feedback, task-oriented dialogue systems, end-to-end learning
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