# Cross-lingual Abstract Meaning Representation Parsing

## TL;DR

This paper introduces a cross-lingual AMR parsing method using annotation projection from English to multiple languages, and proposes an evaluation technique that does not require gold annotations in target languages.

## Contribution

It presents a novel annotation projection approach for cross-lingual AMR parsing and a new evaluation method that leverages English gold annotations without needing target language gold standards.

## Key findings

- Promising results for Italian, Spanish, German, and Chinese
- Effective evaluation method without target language gold annotations
- Successful cross-lingual transfer of AMR parsers

## Abstract

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.