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Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
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Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
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In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.
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Cited by 1 Pith paper
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Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
Task-specific decoder parameters outperform fully shared decoder parameters in both supervised and zero-shot multilingual translation performance.
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