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In simultaneous machine translation, the objective is to determine when toproduce a partial translation given a continuous stream of source words, with atrade-off between latency and quality. We propose a neural machine translation(NMT) model that makes dynamic decisions when to continue feeding on input orgenerate output words. The model is composed of two main components: one todynamically decide on ending a source chunk, and another that translates theconsumed chunk. We train the components jointly and in a manner consistent withthe inference conditions. To generate chunked training data, we propose amethod that utilizes word alignment while also preserving enough context. Wecompare models with bidirectional and unidirectional encoders of differentdepths, both on real speech and text input. Our results on the IWSLT 2020English-to-German task outperform a wait-k baseline by 2.6 to 3.7% BLEUabsolute.

Neural Simultaneous Speech Translation Using Alignment-Based Chunking