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        <title>A Graph-Based Neural Model for End-to-End Frame Semantic Parsing</title>
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    <name type="personal">
        <namePart type="given">ZhiChao</namePart>
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    <name type="personal">
        <namePart type="given">Yueheng</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Meishan</namePart>
        <namePart type="family">Zhang</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.314</identifier>
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        <date>2021-11</date>
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            <start>3864</start>
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%0 Conference Proceedings
%T A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
%A Lin, ZhiChao
%A Sun, Yueheng
%A Zhang, Meishan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F lin-etal-2021-graph
%X Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
%R 10.18653/v1/2021.emnlp-main.314
%U https://aclanthology.org/2021.emnlp-main.314/
%U https://doi.org/10.18653/v1/2021.emnlp-main.314
%P 3864-3874

Markdown (Informal)

[A Graph-Based Neural Model for End-to-End Frame Semantic Parsing](https://aclanthology.org/2021.emnlp-main.314/) (Lin et al., EMNLP 2021)

• A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (Lin et al., EMNLP 2021)

ACL

• ZhiChao Lin, Yueheng Sun, and Meishan Zhang. 2021. A Graph-Based Neural Model for End-to-End Frame Semantic Parsing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3864–3874, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.