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Register a new userSHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language Model Pretraining
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Added by
Chenglei Si
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Conventional tokenization methods for Chinese pretrained language models (PLMs) treat each character as an indivisible token (Devlin et al., 2019), which ignores the characteristics of the Chinese writing system. In this work, we comprehensively study the influences of three main factors on the Chinese tokenization for PLM: pronunciation, glyph (i.e., shape), and word boundary. Correspondingly, we propose three kinds of tokenizers: 1) SHUOWEN (meaning Talk Word), the pronunciation-based tokenizers; 2) JIEZI (meaning Solve Character), the glyph-based tokenizers; 3) Word segmented tokenizers, the tokenizers with Chinese word segmentation. To empirically compare the effectiveness of studied tokenizers, we pretrain BERT-style language models with them and evaluate the models on various downstream NLU tasks. We find that SHUOWEN and JIEZI tokenizers can generally outperform conventional single-character tokenizers, while Chinese word segmentation shows no benefit as a preprocessing step. Moreover, the proposed SHUOWEN and JIEZI tokenizers exhibit significantly better robustness in handling noisy texts. The code and pretrained models will be publicly released to facilitate linguistically informed Chinese NLP.
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Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.
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