神经机器翻译的联合训练 pdf下载

●1 Neural Machine Translation
1.1 Introduction
1.2 Neural Machine Translation
References
2 Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation
2.1 Introduction
2.2 Agreement-Based Joint Training
2.3 Experiments
2.3.1 Setup
2.3.2 Comparison of Loss Functions
2.3.3 Results on Chinese-English Translation
2.3.4 Results on Chinese-English Alignment
2.3.5 Analysis of Alignment Matrices
2.3.6 Results on English-to-French Translation
2.4 Summary
References
3 Semi-supervised Learning for Neural Machine Translation
3.1 Introduction
3.2 Semi-supervised Learning for Neural Machine Translation
3.2.1 Supervised Learning
3.2.2 Autoencoders on Monolingual Corpora
3.2.3 Semi-supervised Learning
3.2.4 Training
3.3 Experiments
3.3.1 Setup
3.3.2 Effect of Sample Size k
3.3.3 Effect of OOV Ratio
3.3.4 Comparison with SMT
3.3.5 Comparison with Previous Work
3.4 Summary
References
4 Joint Training for Pivot-Based Neural Machine Translation
4.1 Introduction
4.2 Pivot-Based NMT
4.3 joint Training for Pivot-Based NMT
4.3.1 Training Objective
4.3.2 Connection Terms
4.3.3 Training
4.4 Experiments
4.4.1 Setup
4.4.2 Results on the Europarl Corpus
4.4.3 Results on the WMT Corpus
4.4.4 Effect of Bridging Corpora
4.5 Summary
References
5 Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning
5.1 Introduction
5.2 Unidirectional Neural Machine Translation
5.3 Bidirectional Neural Machine Translation
5.4 Decoding Strategies
5.5 Experiments
5.5.1 Setup
5.5.2 Effect of Translation Strategies
5.5.3 Comparison with SMT and Standard NMT
5.5.4 BLEU Scores Over Sentence Length
5.5.5 Comparison of Learning Curves
5.5.6 Analysis of Expected Embeddings
5.5.7 Results on English-German Translation
5.6 Summary
References
6 Related Work
6.1 Atentional Mechanisms in Neural Machine Translation
6.2 Capturing Bidirectional Dependencies
6.2.1 Capturing Bidirectional Dependencies
6.2.2 Agreement-Based Learning
6.3 Incorporating Additional Data Resources
6.3.1 Exploiting Monolingual Corpora for Machine Translation
6.3.2 Autoencoders in Unsupervised and Semi-supervised Learning
6.3.3 Machine Translation with Pivot Languages
6.4 Contrastive Learning
References
7 Conclusion
7.1 Conclusion
7.2 Future Directions
7.2.1 Joint Modeling
7.2.2 Joint Training
7.2.3 More Tasks
References