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        <title>Cross-domain Generalization for AMR Parsing</title>
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        <namePart type="given">Xuefeng</namePart>
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        <namePart type="given">Sen</namePart>
        <namePart type="family">Yang</namePart>
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    <name type="personal">
        <namePart type="given">Leyang</namePart>
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        <namePart type="given">Linfeng</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on improving the performance in the specific domain, ignoring the potential domain dependence of AMR parsing systems. To address this, we extensively evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain AMR parsing. We observe that challenges to cross-domain AMR parsing mainly arise from the distribution shift of words and AMR concepts. Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively. Experimental results on two out-of-domain test sets show the superiority of our method.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.749</identifier>
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%0 Conference Proceedings
%T Cross-domain Generalization for AMR Parsing
%A Bai, Xuefeng
%A Yang, Sen
%A Cui, Leyang
%A Song, Linfeng
%A Zhang, Yue
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F bai-etal-2022-cross
%X Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on improving the performance in the specific domain, ignoring the potential domain dependence of AMR parsing systems. To address this, we extensively evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain AMR parsing. We observe that challenges to cross-domain AMR parsing mainly arise from the distribution shift of words and AMR concepts. Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively. Experimental results on two out-of-domain test sets show the superiority of our method.
%R 10.18653/v1/2022.emnlp-main.749
%U https://aclanthology.org/2022.emnlp-main.749
%U https://doi.org/10.18653/v1/2022.emnlp-main.749
%P 10907-10921

Markdown (Informal)

[Cross-domain Generalization for AMR Parsing](https://aclanthology.org/2022.emnlp-main.749) (Bai et al., EMNLP 2022)

• Cross-domain Generalization for AMR Parsing (Bai et al., EMNLP 2022)

ACL

• Xuefeng Bai, Sen Yang, Leyang Cui, Linfeng Song, and Yue Zhang. 2022. Cross-domain Generalization for AMR Parsing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10907–10921, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.