Self-Ensemble: Integration of <i>N</i>-best Hypotheses by Lexically Constrained Decoding
Author:
Affiliation:
1. Graduate School of Information Science and Technology, Osaka University
2. Graduate School of Science and Engineering, Ehime University
Publisher
Association for Natural Language Processing
Link
https://www.jstage.jst.go.jp/article/jnlp/31/2/31_680/_pdf
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2. Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., and Gašić, M. (2018). “MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5016–5026.
3. Chatterjee, R., Negri, M., Turchi, M., Federico, M., Specia, L., and Blain, F. (2017). “Guiding Neural Machine Translation Decoding with External Knowledge.” In Proceedings of the Conference on Machine Translation (WMT), pp. 157–168.
4. Dehghan, M., Kumar, D., and Golab, L. (2022). “GRS: Combining Generation and Revision in Unsupervised Sentence Simplification.” In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 949–960.
5. Eikema, B. and Aziz, W. (2022). “Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 10978–10993.
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