Augmenting Data to Improve Incremental Japanese-English Sentence and Sentence-final Verb Translation
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2021
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Haverford College. Department of Computer Science
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Thesis
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Award
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eng
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Tri-College users only until 2022-01-01, afterwards Open Access.
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Abstract
Final verb prediction has been shown to help expedite simultaneous machine translation when translating between languages with different word orders. Prediction allows a system to begin translating earlier because it has access to information that it otherwise would need to wait for to begin translating. Specifically, if the system is able to predict the final verb in a SOV sentence, it allows the system to start translating much earlier. This thesis examines current prediction mechanisms in neural machine translation models to determine what factors improve predictions between SOV and SVO language. We first train a neural machine translation model to establish how well it can predict the English verb corresponding to the final Japanese verb. We then train new models, with modified data to see it's impact on the model's ability to predict the English verb. We found that the model predicts the English verbs more accurately as more of the Japanese Sentence is revealed. Shuffling the preverb context as well adding subsentences to training data both improved the ability of the model to predict the English verb as well.