Tree-to-Sequence Attentional Neural Machine Translation

TL;DR

This paper introduces a novel neural machine translation model that incorporates source-side syntactic phrase structures with attention, significantly improving translation quality over traditional sequence-to-sequence models.

Contribution

The paper presents an end-to-end tree-to-sequence NMT model that integrates syntactic information via phrase structures and an attention mechanism, advancing the state-of-the-art in translation accuracy.

Findings

Outperforms standard sequence-to-sequence NMT models

Achieves comparable results to state-of-the-art tree-to-string SMT systems

Demonstrates the effectiveness of syntactic information in NMT

Abstract

Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. Experimental results on the WAT'15 English-to-Japanese dataset demonstrate that our proposed model considerably outperforms sequence-to-sequence attentional NMT models and compares favorably with the state-of-the-art tree-to-string SMT system.