@inproceedings{martinez-lorenzo-etal-2023-cross,
    title = "Cross-lingual {AMR} Aligner: Paying Attention to Cross-Attention",
    author = "Mart{\'\i}nez Lorenzo, Abelardo Carlos  and
      Huguet Cabot, Pere Llu{\'\i}s  and
      Navigli, Roberto",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.109",
    doi = "10.18653/v1/2023.findings-acl.109",
    pages = "1726--1742",
    abstract = "This paper introduces a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, and is thus capable of aligning units and spans in sentences of different languages. Our approach leverages modern Transformer-based parsers, which inherently encode alignment information in their cross-attention weights, allowing us to extract this information during parsing. This eliminates the need for English-specific rules or the Expectation Maximization (EM) algorithm that have been used in previous approaches. In addition, we propose a guided supervised method using alignment to further enhance the performance of our aligner. We achieve state-of-the-art results in the benchmarks for AMR alignment and demonstrate our aligner{'}s ability to obtain them across multiple languages. Our code will be available at [\url{https://www.github.com/babelscape/AMR-alignment}](\url{https://www.github.com/babelscape/AMR-alignment}).",
}

This paper introduces a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, and is thus capable of aligning units and spans in sentences of different languages. Our approach leverages modern Transformer-based parsers, which inherently encode alignment information in their cross-attention weights, allowing us to extract this information during parsing. This eliminates the need for English-specific rules or the Expectation Maximization (EM) algorithm that have been used in previous approaches. In addition, we propose a guided supervised method using alignment to further enhance the performance of our aligner. We achieve state-of-the-art results in the benchmarks for AMR alignment and demonstrate our aligner{'}s ability to obtain them across multiple languages. Our code will be available at [\url{https://www.github.com/babelscape/AMR-alignment}](\url{https://www.github.com/babelscape/AMR-alignment}).
Mart{\'\i}nez Lorenzo, Abelardo Carlos Huguet Cabot, Pere Llu{\'\i}s Navigli, Roberto
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention