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Semantic Neural Machine Translation using AMR

arXiv 1902.07282 ↗First posted 19 Feb 2019cs.CL cs.AIcited by 37

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Abstract (arXiv)

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2019.

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Citing papers per year: 2021: 12; 2022: 9; 2023: 7; 2024: 4; 2025: 4; 2026: 1.

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  1. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  2. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  3. 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
  4. 2022DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulationscited by 25
  5. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  6. 2021Semantic Representation for Dialogue Modelingcited by 24
  7. 2021Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answeringcited by 17
  8. 2021Exploiting Reasoning Chains for Multi-hop Science Question Answeringcited by 15
  9. 2024Analyzing the Role of Semantic Representations in the Era of Large Language Modelscited by 12
  10. 2023Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representationcited by 10
  11. 2023AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generationcited by 10
  12. 2022Better Smatch = Better Parser? AMR evaluation is not so simple anymorecited by 9
25 more
  1. 2024Natural Language Processing RELIES on Linguisticscited by 9
  2. 2021Multilingual AMR Parsing with Noisy Knowledge Distillationcited by 8
  3. 2022Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translationcited by 7
  4. 2023Incorporating Graph Information in Transformer-based AMR Parsingcited by 7
  5. 2022T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representationcited by 6
  6. 2021Semantics-aware Attention Improves Neural Machine Translationcited by 5
  7. 2021Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderingscited by 4
  8. 2024A Systematic Review of Data-to-Text NLGcited by 4
  9. 2023AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsingcited by 3
  10. 2022Cross-domain Generalization for AMR Parsingcited by 2
  11. 2023Neural Machine Translation with Dynamic Graph Convolutional Decodercited by 2
  12. 2024Artificial intelligence contribution to translation industry: looking back and forwardcited by 2
  13. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  14. 2025DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Frameworkcited by 2
  15. 2025Concept than Document: Context Compression via AMR-based Conceptual Entropycited by 2
  16. 2021Enhancing the Transformer Decoder with Transition-based Syntaxcited by 1
  17. 2021Making Better Use of Bilingual Information for Cross-Lingual AMR Parsingcited by 1
  18. 2021Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networkscited by 1
  19. 2021Self-Attentive Constituency Parsing for UCCA-based Semantic Parsingcited by 0
  20. 2022Transition-based Abstract Meaning Representation Parsing with Contextual Embeddingscited by 0
  21. 2022Cross-lingual AMR Aligner: Paying Attention to Cross-Attentioncited by 0
  22. 2023Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunitiescited by 0
  23. 2023Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correctioncited by 0
  24. 2025Evaluation of Finetuned LLMs in AMR Parsingcited by 0
  25. 2026CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstructioncited by 0