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 often do not correlate with human judgment, making them unreliable for evaluation and training.
“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]
Section 4.5, “Human User Evaluations” (page not supplied), reports ratings by human judges, while section 4.4 reports task-success and dialogue-state metrics. Neither compares automated metrics with human judgments or says they are unreliable for evaluation or training. Searches for BLEU, ROUGE, and correlation found no passage addressing that claim. (LLM, conf=0.75)
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Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning
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A Survey on the Recent Advancements in Human-Centered Dialog Systems
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Deep Learning for Dialogue Systems
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How to Tame Your Data: Data Augmentation for Dialog State Tracking
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Chatbot Based on Emotions Using Deep Learning
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- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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