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 \href{https://www.github.com/Babelscape/AMR-alignment}{github.com/Babelscape/AMR-alignment}.

翻译：本文提出了一种新型的抽象语义表示（AMR）图对齐器，能够跨语言扩展，从而对齐不同语言句子中的单元和跨句片段。我们的方法利用了基于Transformer的现代解析器，这些解析器本质上在其交叉注意力权重中编码了对齐信息，使我们能够在解析过程中提取这些信息。这消除了以往方法中对英语特定规则或期望最大化（EM）算法的依赖。此外，我们提出了一种使用对齐的引导式监督方法，以进一步增强对齐器的性能。我们在AMR对齐基准测试中取得了最先进的结果，并证明了对齐器能够在多种语言中获得这些结果。我们的代码将发布于\href{https://www.github.com/Babelscape/AMR-alignment}{github.com/Babelscape/AMR-alignment}。