Unsupervised Vocabulary Selection for Domain-Independent Simultaneous Lecture Translation

In this work, we investigate methods to automatically adapt our simultaneous lecture
translation systems to the diverse topics that occur in educational lectures. Utilizing ma-
terials that are available before the lecture begins, such as lecture slides, our proposed
framework iteratively searches for related documents on the World Wide Web and gen-
erates lecture-specific models and vocabularies based on the resulting documents. In
this paper, we propose a novel method for vocabulary selection, a critical aspect of si-
multaneous translation systems where the occurrence of out-of-vocabulary words signifi-
cantly degrades intelligibility. We propose a novel approach based on feature-based rank-
ing and evaluate the effectiveness of 21 different features and their combinations for this
task. On the interACT German-English simultaneous lecture translation system our pro-
posed approach significantly improved vocabulary coverage, reducing out-of-vocabulary
rate, on average by 60% and up to 84%, compared to a lecture-independent baseline. Fur-
thermore, a 40k vocabulary selected using our method obtained better coverage than a
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lecture-independent 300k vocabulary, improving intelligibility and reducing the latency of
the end-to-end system.