Pinyin as subword unit for Chinese-sourced neural machine translation

Unknown word (UNK) or open vocabulary is a challenging problem for neural machine translation (NMT). For alphabetic languages such as English, German and French, transforming a word into subwords is an effective way to alleviate the UNK problem, such as the Byte Pair encoding (BPE) algorithm. However, for the stroke-based languages, such as Chinese, aforementioned method is not effective enough for translation quality. In this paper, we propose to utilize Pinyin, a romanization system for Chinese characters, to convert Chinese characters to subword units to alleviate the UNK problem. We first investigate that how Pinyin and its four diacritics denoting tones affect translation performance of NMT systems, and then propose different strategies to utilise Pinyin and tones as input factors for Chinese–English NMT. Extensive experiments conducted on Chinese–English translation demonstrate that the proposed methods can remarkably improve the translation quality, and can effectively alleviate the UNK problem for Chinese-sourced translation

Neural Machine Translation of Rare Words with Subword Units

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively.Comment: accepted at ACL 2016; new in this version: figure