Abstract
Popular Neural Machine Translation model training uses strategies like backtranslation to improve BLEU scores, requiring large amounts of additional data and training. We introduce a class of conditional generative-discriminative hybrid losses that we use to fine-tune a trained machine translation model. Through a combination of targeted fine-tuning objectives and intuitive re-use of the training data the model has failed to adequately learn from, we improve the model performance of both a sentence-level and a contextual model without using any additional data. We target the improvement of pronoun translations through our fine-tuning and evaluate our models on a pronoun benchmark testset. Our sentence-level model shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets, while our contextual model achieves the best results, improving from 31.81 to 32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En testset, with corresponding improvements in pronoun translation. We further show the generalizability of our method by reproducing the improvements on two additional language pairs, Fr-En and Cs-En.- Anthology ID:
- 2020.emnlp-main.177
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2267–2279
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.177
- DOI:
- 10.18653/v1/2020.emnlp-main.177
- Cite (ACL):
- Prathyusha Jwalapuram, Shafiq Joty, and Youlin Shen. 2020. Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2267–2279, Online. Association for Computational Linguistics.
- Cite (Informal):
- Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses (Jwalapuram et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.emnlp-main.177.pdf
- Code
- ntunlp/pronoun-finetuning