# Tree-to-Sequence Attentional Neural Machine Translation

arXiv: https://arxiv.org/abs/1603.06075 · HTML: https://mlsanity.com/paper/1603.06075/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2016.

## Built on by (most cited first, 28 of 28)

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- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
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- [Code Structure Guided Transformer for Source Code Summarization](https://mlsanity.com/paper/2104.09340/) (2021)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Source Code Summarization with Structural Relative Position Guided Transformer](https://mlsanity.com/paper/2202.06521/) (2022)
- [Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis](https://mlsanity.com/paper/2407.05388/) (2024)
- [AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization](https://mlsanity.com/paper/2112.01184/) (2021)
- [On the Difficulty of Translating Free-Order Case-Marking Languages](https://mlsanity.com/paper/2107.06055/) (2021)
- [Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization](https://mlsanity.com/paper/2305.11074/) (2023)
- [GypSum: Learning Hybrid Representations for Code Summarization](https://mlsanity.com/paper/2204.12916/) (2022)
- [Comparative Code Structure Analysis using Deep Learning for Performance Prediction](https://mlsanity.com/paper/2102.07660/) (2021)
- [Neural Machine Translation with Dynamic Graph Convolutional Decoder](https://mlsanity.com/paper/2305.17698/) (2023)
- [CSA-Trans: Code Structure Aware Transformer for AST](https://mlsanity.com/paper/2404.05767/) (2024)
- [The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey](https://mlsanity.com/paper/2409.02139/) (2024)
- [Sneaking Syntax into Transformer Language Models with Tree Regularization](https://mlsanity.com/paper/2411.18885/) (2024)
- [Learning with Latent Structures in Natural Language Processing: A Survey](https://mlsanity.com/paper/2201.00490/) (2022)
- [Enhancing Neural Machine Translation with Semantic Units](https://mlsanity.com/paper/2310.11360/) (2023)
- [Enhancing the Transformer Decoder with Transition-based Syntax](https://mlsanity.com/paper/2101.12640/) (2021)
- [Unsupervised Learning of Explainable Parse Trees for Improved Generalisation](https://mlsanity.com/paper/2104.04998/) (2021)
- [Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network](https://mlsanity.com/paper/2111.11707/) (2021)
- [Dependency-based Mixture Language Models](https://mlsanity.com/paper/2203.10256/) (2022)
- [Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies](https://mlsanity.com/paper/2203.13550/) (2022)
- [LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition](https://mlsanity.com/paper/2204.09654/) (2022)
- [Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models](https://mlsanity.com/paper/2505.09062/) (2025)
- [AST-MHSA : Code Summarization using Multi-Head Self-Attention](https://mlsanity.com/paper/2308.05646/) (2023)
- [Top-down string-to-dependency Neural Machine Translation](https://mlsanity.com/paper/2603.27938/) (2026)

## Abstract (arXiv)

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.