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A Diversity-Promoting Objective Function for Neural Conversation Models
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Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Moses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400
Maximum mutual information estimation of hidden Markov model parameters for speech recognition
Bahl, L., Brown, P., de Souza, P., & Mercer, R. (2005) · ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1986.1169179 · Cited by 817
Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014) · arXiv · doi:10.48550/arxiv.1412.7449 · Cited by 464
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Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
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Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177
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Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.30 · Cited by 1,076
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Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., & Gašić, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1547 · Cited by 1,028
How Can We Know What Language Models Know?
Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00324 · Cited by 1,002
A Persona-Based Neural Conversation Model
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898
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Zhang, H., Song, H., Li, S., Zhou, M., & Song, D. (2024) · ACM Computing Surveys · doi:10.1145/3617680 · Cited by 317
Challenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288
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Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv · doi:10.48550/arxiv.2210.03350 · Cited by 294
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Guu, K., Hashimoto, T. B., Oren, Y., & Liang, P. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00030 · Cited by 300
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Kikuchi, Y., Neubig, G., Sasano, R., Takamura, H., & Okumura, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1140 · Cited by 222
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Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207
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Hashimoto, T. B., Zhang, H., & Liang, P. (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/n19-1169 · Cited by 161
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Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159
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Kulikov, I., Miller, A., Cho, K., & Weston, J. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8609 · Cited by 83
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Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63
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Wiher, G., Meister, C., & Cotterell, R. (2022) · arXiv · doi:10.48550/arxiv.2203.15721 · Cited by 48
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Wang, Q., Fu, Y., Cao, Y., Wang, S., Tian, Z., & Ding, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130193 · Cited by 55
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Khalifa, M., Elsahar, H., & Dymetman, M. (2020) · arXiv · doi:10.48550/arxiv.2012.11635 · Cited by 39
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Wang, Z., Lan, A., & Baraniuk, R. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.484 · Cited by 39
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Fu, L., Newman, B., Jakesch, M., & Kreps, S. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581351 · Cited by 31
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Bae, S., Kwak, D., Kang, S., Lee, M. Y., Kim, S., Jeong, Y., Kim, H., Lee, S.-W., Park, W., & Sung, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.276 · Cited by 29
Yi, S., Goel, R., Khatri, C., Cervone, A., Chung, T., Hedayatnia, B., Venkatesh, A., Gabriel, R., & Hakkani-Tur, D. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8608 · Cited by 26
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Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., & Dolan, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21429 · Cited by 27
Self-citationClark, E., & Smith, N. A. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.279 · Cited by 23
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Zhang, T., Liu, Y., Li, B., Zeng, Z., Wang, P., You, Y., Miao, C., & Cui, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.247 · Cited by 14
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Kang, M., Kwak, J. M., Baek, J., & Hwang, S. J. (2023) · arXiv · doi:10.48550/arxiv.2305.18846 · Cited by 13
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Zhao, J., Fang, M., Shi, Z., Li, Y., Chen, L., & Pechenizkiy, M. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.757 · Cited by 14
Towards User-Centric Text-to-Text Generation: A Survey
Yang, D., & Flek, L. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-83527-9_1 · Cited by 10
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., & Yih, S. W.-T. (2023) · arXiv · doi:10.48550/arxiv.2305.14739 · Cited by 6
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Deng, H., & Raffel, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.721 · Cited by 5
ODDA: An OODA-Driven Diverse Data Augmentation Framework for Low-Resource Relation Extraction
Zhong, Y., Gao, Y., Zhang, X., & Wang, H. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.15 · Cited by 3
“Following the previous work (Zheng et al., 2024), we introduce the Distinct (Li et al., 2016) to assess the diversity of synthetic sentences.”
InfluentialAutomatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator
Liao, Y., Meng, Y., Wang, Y., Liu, H., Wang, Y., & Wang, Y. (2024) · arXiv · doi:10.48550/arxiv.2403.08495 · Cited by 15
A Prompt Chaining Framework for Long-Term Recall in LLM-Powered Intelligent Assistant
Seo, S., Yoo, S., & Jang, Y. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712117 · Cited by 3
Rahman, A., Yu, A., & Cho, K. (2026) · Systems · doi:10.3390/systems14020175 · Cited by 2
Evaluating the Diversity and Quality of LLM Generated Content
Shypula, A., Li, S., Zhang, B., Padmakumar, V., Yin, K., & Bastani, O. (2025) · arXiv · doi:10.48550/arxiv.2504.12522 · Cited by 1
Paech, S., Roush, A., Goldfeder, J., & Shwartz-Ziv, R. (2025) · arXiv · doi:10.48550/arxiv.2510.15061
“Computed as the mean of MATTR-500 (moving-window TTR, 500-token window), Root-TTR ( V/ √ N ), HD-D (expected unique-word rate from random subsamples), and Distinct-1/2/3 (unique n-grams / total tokens) [Guiraud, 1960, McCarthy and Jarvis, 2010, Li et al., 2016].”
Metadata
- Authors
- Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B.
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Pages
- 110-119
- OpenAlex
- W2963206148
- Cited by
- 1,926
- References
- 7
- FWCI
- 88.40
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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