Mixed Cross Entropy Loss for Neural Machine Translation

Haoran Li, Wei Lu
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6425-6436, 2021.

Abstract

In neural machine translation, Cross Entropy loss (CE) is the standard loss function in two training methods of auto-regressive models, i.e., teacher forcing and scheduled sampling. In this paper, we propose mixed Cross Entropy loss (mixed CE) as a substitute for CE in both training approaches. In teacher forcing, the model trained with CE regards the translation problem as a one-to-one mapping process, while in mixed CE this process can be relaxed to one-to-many. In scheduled sampling, we show that mixed CE has the potential to encourage the training and testing behaviours to be similar to each other, more effectively mitigating the exposure bias problem. We demonstrate the superiority of mixed CE over CE on several machine translation datasets, WMT’16 Ro-En, WMT’16 Ru-En, and WMT’14 En-De in both teacher forcing and scheduled sampling setups. Furthermore, in WMT’14 En-De, we also find mixed CE consistently outperforms CE on a multi-reference set as well as a challenging paraphrased reference set. We also found the model trained with mixed CE is able to provide a better probability distribution defined over the translation output space. Our code is available at https://github.com/haorannlp/mix.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-li21n, title = {Mixed Cross Entropy Loss for Neural Machine Translation}, author = {Li, Haoran and Lu, Wei}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {6425--6436}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/li21n/li21n.pdf}, url = {https://proceedings.mlr.press/v139/li21n.html}, abstract = {In neural machine translation, Cross Entropy loss (CE) is the standard loss function in two training methods of auto-regressive models, i.e., teacher forcing and scheduled sampling. In this paper, we propose mixed Cross Entropy loss (mixed CE) as a substitute for CE in both training approaches. In teacher forcing, the model trained with CE regards the translation problem as a one-to-one mapping process, while in mixed CE this process can be relaxed to one-to-many. In scheduled sampling, we show that mixed CE has the potential to encourage the training and testing behaviours to be similar to each other, more effectively mitigating the exposure bias problem. We demonstrate the superiority of mixed CE over CE on several machine translation datasets, WMT’16 Ro-En, WMT’16 Ru-En, and WMT’14 En-De in both teacher forcing and scheduled sampling setups. Furthermore, in WMT’14 En-De, we also find mixed CE consistently outperforms CE on a multi-reference set as well as a challenging paraphrased reference set. We also found the model trained with mixed CE is able to provide a better probability distribution defined over the translation output space. Our code is available at https://github.com/haorannlp/mix.} }
Endnote
%0 Conference Paper %T Mixed Cross Entropy Loss for Neural Machine Translation %A Haoran Li %A Wei Lu %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-li21n %I PMLR %P 6425--6436 %U https://proceedings.mlr.press/v139/li21n.html %V 139 %X In neural machine translation, Cross Entropy loss (CE) is the standard loss function in two training methods of auto-regressive models, i.e., teacher forcing and scheduled sampling. In this paper, we propose mixed Cross Entropy loss (mixed CE) as a substitute for CE in both training approaches. In teacher forcing, the model trained with CE regards the translation problem as a one-to-one mapping process, while in mixed CE this process can be relaxed to one-to-many. In scheduled sampling, we show that mixed CE has the potential to encourage the training and testing behaviours to be similar to each other, more effectively mitigating the exposure bias problem. We demonstrate the superiority of mixed CE over CE on several machine translation datasets, WMT’16 Ro-En, WMT’16 Ru-En, and WMT’14 En-De in both teacher forcing and scheduled sampling setups. Furthermore, in WMT’14 En-De, we also find mixed CE consistently outperforms CE on a multi-reference set as well as a challenging paraphrased reference set. We also found the model trained with mixed CE is able to provide a better probability distribution defined over the translation output space. Our code is available at https://github.com/haorannlp/mix.
APA
Li, H. & Lu, W.. (2021). Mixed Cross Entropy Loss for Neural Machine Translation. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6425-6436 Available from https://proceedings.mlr.press/v139/li21n.html.

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