Inference-only sub-character decomposition improves translation of unseen logographic characters

• URL: http://arxiv.org/abs/2011.06523v1
• Date: Thu, 12 Nov 2020 17:36:22 GMT
• Title: Inference-only sub-character decomposition improves translation of unseen logographic characters
• Authors: Danielle Saunders, Weston Feely, Bill Byrne
• Abstract summary: Neural Machine Translation (NMT) on logographic source languages struggles when translating unseen' characters. We investigate existing ideograph-based sub-character decomposition approaches for Chinese-to-English and Japanese-to-English NMT. We find that complete sub-character decomposition often harms unseen character translation, and gives inconsistent results generally.
• Score: 18.148675498274866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Machine Translation (NMT) on logographic source languages struggles when translating `unseen' characters, which never appear in the training data. One possible approach to this problem uses sub-character decomposition for training and test sentences. However, this approach involves complete retraining, and its effectiveness for unseen character translation to non-logographic languages has not been fully explored. We investigate existing ideograph-based sub-character decomposition approaches for Chinese-to-English and Japanese-to-English NMT, for both high-resource and low-resource domains. For each language pair and domain we construct a test set where all source sentences contain at least one unseen logographic character. We find that complete sub-character decomposition often harms unseen character translation, and gives inconsistent results generally. We offer a simple alternative based on decomposition before inference for unseen characters only. Our approach allows flexible application, achieving translation adequacy improvements and requiring no additional models or training.

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