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Cross-attention is an important component of neural machine translation(NMT), which is always realized by dot-product attention in previous methods.However, dot-product attention only considers the pair-wise correlation betweenwords, resulting in dispersion when dealing with long sentences and neglect ofsource neighboring relationships. Inspired by linguistics, the above issues arecaused by ignoring a type of cross-attention, called concentrated attention,which focuses on several central words and then spreads around them. In thiswork, we apply Gaussian Mixture Model (GMM) to model the concentrated attentionin cross-attention. Experiments and analyses we conducted on three datasetsshow that the proposed method outperforms the baseline and has significantimprovement on alignment quality, N-gram accuracy, and long sentencetranslation.

Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model