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Training Data Annotation for Segmentation Classification in Simultaneous Translation

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Thesis type
(Thesis) M.Sc.
Date created
Segmentation of the incoming speech stream and translating segments incrementally is a commonly used technique that improves latency in spoken language translation. Previous work of Oda et al. 2014 [1] has explored creating training data for segmentation by finding segments that maximize translation quality with a user-defined bound on segment length.In this work, we provide a new algorithm that uses Pareto-optimality to find good segment boundaries that can balance the trade-off between latency versus translation quality. We compare against the state-of-the-art greedy algorithm from Oda et al. 2014. Our experimental results show that we can improve latency by up to 12% without harming theBleuscore for the same average segment length. Another benefit is that for any segment size,Pareto-optimal segments maximize both latency and translation quality.
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Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Sarkar, Anoop
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etd9597_SShavarani.pdf 1.15 MB

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