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Neural Machine Translation of Rare Words with Subword Units

Summary

This research paper focuses on improving the translation of rare and unseen words in neural machine translation (NMT) systems by encoding words as sequences of subword units. The authors argue that using a fixed vocabulary for NMT models limits their ability to translate words not encountered during training. To address this, they propose using a technique called byte pair encoding (BPE) to segment words into smaller units, such as morphemes or phonemes, which can then be translated and combined to form new words. The paper explores various segmentation techniques and empirically demonstrates that subword models significantly outperform baseline systems, especially in the translation of rare and unseen words, including names and compounds.

原文链接：https://arxiv.org/abs/1508.07909