mlsanityData to 7 Oct 2026

Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing

arXiv 2106.04565 ↗First posted 8 Jun 2021cs.CL cs.AIcited by 15

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Abstract (arXiv)

In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to capture its core semantic content through concepts connected by manifold types of semantic relations. Methods typically leverage large silver training data to learn a single model that is able to project non-English sentences to AMRs. However, we find that a simple baseline tends to be over-looked: translating the sentences to English and projecting their AMR with a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this simple two-step base-line, and enhance it with a strong NMT system and a strong AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages: German, Italian, Spanish and Mandarin with +14.6, +12.6, +14.3 and +16.0 Smatch points.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

14 of 29 references.

References · 14

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  4. 2020AMR Parsing via Graph-Sequence Iterative Inferencecited by 47
  5. 2018AMR Parsing as Graph Prediction with Latent Alignmentcited by 36
  6. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  7. 2020Improving AMR Parsing with Sequence-to-Sequence Pre-trainingcited by 30
  8. 2019Core Semantic First: A Top-down Approach for AMR Parsingcited by 24
  9. 2020AMR Similarity Metrics from Principlescited by 21
  10. 2017Cross-lingual Abstract Meaning Representation Parsingcited by 19
  11. 2021Zero-Shot Cross-lingual Semantic Parsingcited by 19
  12. 2017Nematus: a Toolkit for Neural Machine Translationcited by 16
2 more
  1. 2020Multilingual AMR-to-Text Generationcited by 12
  2. 2021Bootstrapping Multilingual AMR with Contextual Word Alignmentscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 0; 2025: 2; 2026: 1.

15 papers.

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  1. 2022SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Featurescited by 33
  2. 2021Maximum Bayes Smatch Ensemble Distillation for AMR Parsingcited by 27
  3. 2023SMATCH++: Standardized and Extended Evaluation of Semantic Graphscited by 19
  4. 2021Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similaritycited by 15
  5. 2023X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agentscited by 12
  6. 2022Better Smatch = Better Parser? AMR evaluation is not so simple anymorecited by 9
  7. 2021Multilingual AMR Parsing with Noisy Knowledge Distillationcited by 8
  8. 2025Sentence Smith: Controllable Edits for Evaluating Text Embeddingscited by 4
  9. 2022Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representationcited by 2
  10. 2022Bootstrapping Multilingual Semantic Parsers using Large Language Modelscited by 2
  11. 2022SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distancecited by 0
  12. 2022Cross-lingual AMR Aligner: Paying Attention to Cross-Attentioncited by 0
3 more
  1. 2023AMR4NLI: Interpretable and robust NLI measures from semantic graphscited by 0
  2. 2025When Does Meaning Backfire? Investigating the Role of AMRs in NLIcited by 0
  3. 2026Effective QA-driven Annotation of Predicate-Argument Relations Across Languagescited by 0