The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
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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Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.9883 · Cited by 1,645
A Network-based End-to-End Trainable Task-oriented Dialogue System
Wen, T.-H., Vandyke, D., Mrkšić, N., Gašić, M., Rojas-Barahona, L. M., Su, P.-H., Ultes, S., & Young, S. J. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1042 · Cited by 1,017
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A Persona-Based Neural Conversation Model
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The Second Dialog State Tracking Challenge
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Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
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Research works printed but not linked: 4
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- Matthew Henderson, Blaise Thomson, and Steve Young. 2014b. Robust dialog state tracking using delexicalised recurrent neural networks and unsu- pervised gate. In IEEE SLT.
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Cited by
Sorted by citations
A Simple Language Model for Task-Oriented DialoguePreprint
Hosseini-Asl, E., McCann, B., Wu, C.-S., Yavuz, S., & Socher, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00796 · Cited by 398
Survey on evaluation methods for dialogue systems
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09866-x · Cited by 361
A survey on empathetic dialogue systems
Ma, Y., Nguyen, K. L., Xing, F., & Cambria, E. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.011 · Cited by 230
ConveRT: Efficient and Accurate Conversational Representations from Transformers
Henderson, M., Casanueva, I., Mrkšić, N., Su, P.-H., Wen, T.-H., & Vulić, I. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.196 · Cited by 204
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
Hancock, B., Bordes, A., Mazare, P.-E., & Weston, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1358 · Cited by 161
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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