Semi-supervised Learning for Neural Machine Translation Yong Cheng - PowerPoint PPT Presentation

Jul 17, 2023 •371 likes •650 views

Semi-supervised Learning for Neural Machine Translation Yong Cheng joint work with Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu 1 Machine Translation Automated translation using computer software 2 Machine Translation Rule-based

Semi-supervised Learning for Neural Machine Translation Yong Cheng joint work with Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu 1
Machine Translation Automated translation using computer software 2
Machine Translation Rule-based Machine Translation 1970s Example-based Machine Translation 1984 Statical Machine Translation (SMT) 1993 Neural Machine Translation � NMT � 2014 Trends: learning to translate from DATA 3
Machine Translation Parallel corpora are usually limited in & & quantity quality coverage Monolingual Corpora Parallel Corpora 4
Monolingual Corpora Used in SMT and NMT N-gram language model in SMT Koehn et al., [2007] Monolingual corpora as decipherment Ravi and Knight [2011] Integrate a neural language model into NMT. Gulccehre et al. [2015] Additional pseudo parallel corpus. Sennrich et al. [2016] 5
Supervised Training Parallel Corpus Objective 6
Unsupervised Training Monolingual Corpus 7
cc Our Approach — Autoencoders x bushi yu shalong juxing le huitan 8
cc Our Approach — Autoencoders ! θ ) P ( y | x ; x bushi yu shalong juxing le huitan 9
cc Our Approach — Autoencoders y latent Bush held a talk with sharon ! θ ) P ( y | x ; x bushi yu shalong juxing le huitan 10
cc Our Approach — Autoencoders ! θ ) P ( x | y ; y latent Bush held a talk with sharon ! θ ) P ( y | x ; x bushi yu shalong juxing le huitan 11
cc Our Approach — Autoencoders ′ x bushi yu shalong juxing le huitan ! θ ) P ( x | y ; y latent Bush held a talk with sharon ! θ ) P ( y | x ; x bushi yu shalong juxing le huitan 12
cc Our Approach — Autoencoders source autoencoder target autoencoder 13
Unsupervised Training (Autoencoders) Monolingual Corpus target autoencoder 14
Semi-supervised Training Training Objective 15
Translation Results Compared with Moses (SMT) and RNNSearch (NMT) 16
Translation Results Compared with Moses (SMT) and RNNSearch (NMT) 17
Translation Results Compared with Moses (SMT) and RNNSearch (NMT) 18
Translation Results Compared with Moses (SMT) and RNNSearch (NMT) 19
Translation Results Compared with Moses (SMT) and RNNSearch (NMT) 20
Translation Results Compared with Sennrich et al. [2015a] 21
Example Translation of Monolingual Corpus 22
Conclusion Monolingual corpora is an important resource for neural machine translation. We have proposed a semi-supervised approach to training bidirectional neural machine translation models for exploiting monolingual corpora. As our method is sensitive to the OOVs present in monolingual corpora, we plan to integrate Jean et al. (2015)’s technique on using very large vocabulary into our approach. 23
Thank You ! 24
Effect of Sample Size ZH-EN EN-ZH 25
Effect of OOV ratio ZH-EN EN-ZH 26

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