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An empirical study on many-to-many simultaneous machine translation

By Erenay Dayanik, Ran Xue, Ching-Yun (Frannie) Chang
2022
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Simultaneous machine translation (SimulMT) is a challenging task which aims to translate a source sequence to the target language with low latency. Despite significant progress in SimulMT, there has not been much work in the area of multilingual SimulMT where a single model is capable of translating between multiple language pairs. This paper studies SimulMT from a multilingual perspective. Through our experiments, we first compare several language tag strategies, and show that language tag strategies can effectively adapt a unidirectional SimulMT model to translate multiple language arcs. Second, we find that SimulMT models trained on a language family perform better than a global model. Finally, we demonstrate that it is possible to improve the performances of multilingual SimulMT models by transferring embeddings from a pre-trained language model such as multilingual BERT.
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US, NY, New York
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US, NJ, Newark
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US, WA, Seattle
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US, WA, Bellevue
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US, WA, Seattle
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