mlsanityData to 7 Oct 2026

Tree-to-Sequence Attentional Neural Machine Translation

arXiv 1603.06075 ↗First posted 19 Mar 2016cs.CLcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2016.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 4; 2024: 4; 2025: 1; 2026: 1.

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  1. 2022Large Language Models Meet NL2Code: A Surveycited by 135
  2. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  3. 2022Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scalecited by 40
  4. 2021Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Informationcited by 31
  5. 2021Code Structure Guided Transformer for Source Code Summarizationcited by 29
  6. 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
  7. 2022Source Code Summarization with Structural Relative Position Guided Transformercited by 14
  8. 2024Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesiscited by 12
  9. 2021AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarizationcited by 11
  10. 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8
  11. 2023Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarizationcited by 6
  12. 2022GypSum: Learning Hybrid Representations for Code Summarizationcited by 5
16 more
  1. 2021Comparative Code Structure Analysis using Deep Learning for Performance Predictioncited by 3
  2. 2024CSA-Trans: Code Structure Aware Transformer for ASTcited by 3
  3. 2024The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Surveycited by 3
  4. 2024Sneaking Syntax into Transformer Language Models with Tree Regularizationcited by 3
  5. 2022Learning with Latent Structures in Natural Language Processing: A Surveycited by 2
  6. 2023Neural Machine Translation with Dynamic Graph Convolutional Decodercited by 2
  7. 2023Enhancing Neural Machine Translation with Semantic Unitscited by 2
  8. 2021Enhancing the Transformer Decoder with Transition-based Syntaxcited by 1
  9. 2021Unsupervised Learning of Explainable Parse Trees for Improved Generalisationcited by 1
  10. 2021Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Networkcited by 1
  11. 2022Dependency-based Mixture Language Modelscited by 1
  12. 2022Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategiescited by 1
  13. 2022LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognitioncited by 1
  14. 2025Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Modelscited by 1
  15. 2023AST-MHSA : Code Summarization using Multi-Head Self-Attentioncited by 0
  16. 2026Top-down string-to-dependency Neural Machine Translationcited by 0