Multitask Learning For Different Subword Segmentations In Neural Machine Translation

Tejas Srinivasan, Ramon Sanabria, Florian Metze


Abstract
In Neural Machine Translation (NMT) the usage of sub􏰃words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simultaneously, removing the need to search for the optimal segmentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can be combined as a post-processing step to give better translations, which improves over hypothesis combination from baseline models while using substantially fewer parameters.
Anthology ID:
2019.iwslt-1.25
Volume:
Proceedings of the 16th International Conference on Spoken Language Translation
Month:
November 2-3
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
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Pages:
Language:
URL:
https://aclanthology.org/2019.iwslt-1.25
DOI:
Bibkey:
Cite (ACL):
Tejas Srinivasan, Ramon Sanabria, and Florian Metze. 2019. Multitask Learning For Different Subword Segmentations In Neural Machine Translation. In Proceedings of the 16th International Conference on Spoken Language Translation, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Multitask Learning For Different Subword Segmentations In Neural Machine Translation (Srinivasan et al., IWSLT 2019)
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PDF:
https://aclanthology.org/2019.iwslt-1.25.pdf