04 Nov 2015

Our paper on “Learning Segmentations that Balance Latency versus Quality in Spoken Language Translation” by Hassan S. Shavarani, Maryam Siahbani, Ramtin Mehdizadeh Seraj and Anoop Sarkar was accepted for publication at the 12th International Workshop on Spoken Language Translation: IWSLT 2015 to be held in Da Nang, Vietnam from December 3-4, 2015.

Abstract: Segmentation of the incoming speech stream and translating segments incrementally is a commonly used technique that improves latency in spoken language translation. Previous work 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, using Pareto-optimality, for finding good segment boundaries that can balance the trade-off between latency versus translation quality. Our experimental results show that we can provide qualitatively better segments that improve latency without substantially hurting translation quality.