Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017

Zi Long, Ryuichiro Kimura, Takehito Utsuro, Tomoharu Mitsuhashi, Mikio Yamamoto


Abstract
Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).
Anthology ID:
W17-5709
Volume:
Proceedings of the 4th Workshop on Asian Translation (WAT2017)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venues:
WAT | WS
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
110–118
Language:
URL:
https://www.aclweb.org/anthology/W17-5709
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5709.pdf