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

2021-09-11 10:01:24
Shaolei Zhang, Yang Feng
arXiv_AI
         
Abstract

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 between words, resulting in dispersion when dealing with long sentences and neglect of source neighboring relationships. Inspired by linguistics, the above issues are caused by ignoring a type of cross-attention, called concentrated attention, which focuses on several central words and then spreads around them. In this work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention in cross-attention. Experiments and analyses we conducted on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation.

Abstract (translated)

URL

https://arxiv.org/abs/2109.05244

PDF

https://arxiv.org/pdf/2109.05244.pdf

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