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Domain Mixing for Chinese-English Translation

Domain Mixing for Chinese-English Translation. Chris Leege. The Project. Goal Translate Chinese novels into English using Neural Machine Translation Challenges Chinese to English translation requires a lot of data There aren’t many Chinese and English parallel corpora

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Domain Mixing for Chinese-English Translation

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  1. Domain Mixing for Chinese-English Translation Chris Leege

  2. The Project • Goal • Translate Chinese novels into English using Neural Machine Translation • Challenges • Chinese to English translation requires a lot of data • There aren’t many Chinese and English parallel corpora • The majority of Chinese and English parallel corpora are in domains other than novels, mostly news, UN reports, or subtitles

  3. Data • Casia2015 Chinese-English parallel corpus • One million parallel sentences from around the web. • Chinese Novel Corpus • Manually aligned • 45,000 parallel sentences • 2,096,000 characters

  4. Models Pure Casia2015 corpus Pure novel corpus Naïve mixed corpus Mixed corpus with target tokens • Effective Domain Mixing for Neural Machine Translation

  5. Results Figure 2. BLEU Scores for the four models. Models on the y-axis, test data on the x-axis

  6. Results Figure 3. BLEU Scores for the second four models. Models on the y-axis, test data on the x-axis

  7. Conclusion • Possible Issues • Casia2015 too heterogenous • Not enough data • Next Steps • Try again with a larger, more homogenous corpus, such as the UN corpus

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