This document summarizes research on dual learning for machine translation. It discusses using a dual learning algorithm to train translation models on both bilingual data and monolingual data. The researchers evaluate models trained on 10% and 100% bilingual data both with and without additional monolingual data. They find that adding monolingual data through dual learning improves BLEU scores compared to only using bilingual data for training.

1. Presented by:
Sreeja R Thoom
Jayavardhan Reddy Peddamail
Dual Learning for Machine
Translation

5. Hidden Vectors( Encoder):
Decoder Portion:

13. Beam Search

15. Dataset:
● WMTʼ14
● 12M sentence pairs
● English -> French, French -> English
Data usage (for dual learning):
● Small
○ Train translation models with 10% bilingual
data.
○ Train translation models with 10% bilingual
data and monolingual data through dual
learning algorithm.
● Large:
○ Train translation models with 100%
bilingual data.
○ Train translation models with 100%
bilingual data and with monolingual data
through dual learning algorithm.

18. How is Reconstruction BLEU score higher than Translation BLEU Score?

21. Microsoft reaches a historic milestone, using AI to match human performance in translating news from Chinese to English-
https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/

22. Questions:
● Removing all Monolingual sentences with out of vocabulary terms.
● Reconstruction BLEU score> Translation BLEU Score