Syntax-aware neural machine translation directed by syntactic dependency degree

There are various ways to incorporate syntax knowledge into neural machine translation (NMT). However, quantifying the dependency syntactic intimacy (DSI) between word pairs in a dependency tree has not being considered to use in attentional and transformer-based NMT. In this paper, we innovatively propose a variant of Tree-LSTM to capture the syntactic dependency degree (SDD) between word pairs in dependency trees. Two syntax-aware distances, including a tuned syntax distance and a $$\varvec{\rho }$$ -dependent distance, are proposed. For attentional NMT, two syntax-aware attentions based on two syntax-aware distances are proposed for attentional NMT, and we also design a dual attention to simultaneously generate global context and dependency syntactic context. For transformer-based NMT, we explicitly incorporate the dependency syntax into self-attention network (SAN) to propose a syntax-aware SAN. Experiments on IWSLT’17 English–German, IWSLT Chinese–English and WMT’15 English–Finnish translation tasks show that our syntax-aware NMT significantly improves translation quality by comparing with baseline methods, even the state-of-the-art transformer-based NMT.