Enhancing Machine Translation with Dependency-Aware Self-Attention

Abstract

Most neural machine translation models only rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. In this work, we investigate different approaches to incorporate syntactic knowledge in the Transformer model and also propose a novel, parameter-free, dependency-aware self-attention mechanism that improves its translation quality, especially for long sentences and in low-resource scenarios. We show the efficacy of each approach on WMT EnglishGerman and EnglishTurkish, and WAT EnglishJapanese translation tasks.

Topics & Concepts