# Cross-lingual Abstract Meaning Representation Parsing

arXiv: https://arxiv.org/abs/1704.04539 · HTML: https://mlsanity.com/paper/1704.04539/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2017.

## Built on by (most cited first, 19 of 19)

- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04565/) (2021)
- [Bootstrapping Multilingual AMR with Contextual Word Alignments](https://mlsanity.com/paper/2102.02189/) (2021)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [Multilingual Neural Semantic Parsing for Low-Resourced Languages](https://mlsanity.com/paper/2106.03469/) (2021)
- [Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://mlsanity.com/paper/2210.09773/) (2022)
- [Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation](https://mlsanity.com/paper/2109.03808/) (2021)
- [MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing](https://mlsanity.com/paper/2406.01052/) (2024)
- [Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation](https://mlsanity.com/paper/2210.03018/) (2022)
- [Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing](https://mlsanity.com/paper/2410.03357/) (2024)
- [Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04814/) (2021)
- [POTATO: exPlainable infOrmation exTrAcTion framewOrk](https://mlsanity.com/paper/2201.13230/) (2022)
- [Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset](https://mlsanity.com/paper/2205.07712/) (2022)
- [Cross-lingual AMR Aligner: Paying Attention to Cross-Attention](https://mlsanity.com/paper/2206.07587/) (2022)
- [Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability](https://mlsanity.com/paper/2309.06460/) (2023)
- [Evaluating Automatic Metrics with Incremental Machine Translation Systems](https://mlsanity.com/paper/2407.03277/) (2024)
- [HuAMR: A Hungarian AMR Parser and Dataset](https://mlsanity.com/paper/2502.20552/) (2025)
- [ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue](https://mlsanity.com/paper/2508.12819/) (2025)

## Abstract (arXiv)

Abstract Meaning Representation (AMR) annotation efforts have mostly focused on English. In order to train parsers on other languages, we propose a method based on annotation projection, which involves exploiting annotations in a source language and a parallel corpus of the source language and a target language. Using English as the source language, we show promising results for Italian, Spanish, German and Chinese as target languages. Besides evaluating the target parsers on non-gold datasets, we further propose an evaluation method that exploits the English gold annotations and does not require access to gold annotations for the target languages. This is achieved by inverting the projection process: a new English parser is learned from the target language parser and evaluated on the existing English gold standard.