Papers › Improving Robustness of Neural Machine Translation with Multi-task Learning

Improving Robustness of Neural Machine Translation with Multi-task Learning

1 Aug 2019WS 2019 8archive 2025-07-28

Shuyan Zhou, Xiangkai Zeng, Yingqi Zhou, Antonios Anastasopoulos, Graham Neubig

While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.

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Machine Translation Multi-Task Learning NMT Translation

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer

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