The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
Open accessAbstract
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation.This work proposes a new benchmark for empathetic dialogue generation and EMPATHETICDIALOGUES, a novel dataset of 25k conversations grounded in emotional situations.Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data.We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy retraining of the full model.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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Cited by
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1050 · Cited by 1,416
A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
Aligning AI With Shared Human Values
Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., & Steinhardt, J. (2020) · arXiv · doi:10.48550/arxiv.2008.02275 · Cited by 517
Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., & Althoff, T. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-022-00593-2 · Cited by 500
The Caring Machine: Feeling AI for Customer Care
Huang, M.-H., & Rust, R. T. (2024) · Journal of Marketing · doi:10.1177/00222429231224748 · Cited by 312
Metadata
- Authors
- Rashkin, H., Smith, E. M., Li, M., & Boureau, Y.-L.
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 5370-5381
- OpenAlex
- W2950242852
- Cited by
- 771
- References
- 60
- FWCI
- 30.25
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Rashkin, H., Smith, E. M., Li, M., & Boureau, Y.-L. (2019). Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1534
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