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        <title>Constituency Tree Representation for Argument Unit Recognition</title>
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    <name type="personal">
        <namePart type="given">Samuel</namePart>
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        <namePart type="given">Florian</namePart>
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            <title>Proceedings of the 10th Workshop on Argument Mining</title>
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            <namePart type="given">Milad</namePart>
            <namePart type="family">Alshomary</namePart>
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        <name type="personal">
            <namePart type="given">Chung-Chi</namePart>
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    <abstract>The conventional method of extracting arguments from sentences solely relies on word proximity, disregarding the syntactic structure of the sentence. This approach often leads to inaccuracies, especially when identifying argumentative span boundaries. In this research, we investigate the benefits of utilizing a constituency tree representation of sentences to predict Argument Discourse Units (ADUs) at the token level. We first evaluate the effectiveness of utilizing the constituency tree representation for capturing the structural attributes of arguments within sentences. We demonstrate empirically that the constituency structure surpasses simple linear dependencies among neighboring words in terms of effectiveness. Our approach involves leveraging graph neural networks in conjunction with the constituency tree, adapting it specifically for argument unit recognition. Through extensive evaluation, our model outperforms existing approaches in recognizing argument units at the token level. Furthermore, we employ explainability methods to assess the suitability of our model architecture, providing insights into its performance.</abstract>
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    <identifier type="doi">10.18653/v1/2023.argmining-1.4</identifier>
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%0 Conference Proceedings
%T Constituency Tree Representation for Argument Unit Recognition
%A Guilluy, Samuel
%A Mehats, Florian
%A Chouli, Billal
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F guilluy-etal-2023-constituency
%X The conventional method of extracting arguments from sentences solely relies on word proximity, disregarding the syntactic structure of the sentence. This approach often leads to inaccuracies, especially when identifying argumentative span boundaries. In this research, we investigate the benefits of utilizing a constituency tree representation of sentences to predict Argument Discourse Units (ADUs) at the token level. We first evaluate the effectiveness of utilizing the constituency tree representation for capturing the structural attributes of arguments within sentences. We demonstrate empirically that the constituency structure surpasses simple linear dependencies among neighboring words in terms of effectiveness. Our approach involves leveraging graph neural networks in conjunction with the constituency tree, adapting it specifically for argument unit recognition. Through extensive evaluation, our model outperforms existing approaches in recognizing argument units at the token level. Furthermore, we employ explainability methods to assess the suitability of our model architecture, providing insights into its performance.
%R 10.18653/v1/2023.argmining-1.4
%U https://aclanthology.org/2023.argmining-1.4
%U https://doi.org/10.18653/v1/2023.argmining-1.4
%P 35-44

Markdown (Informal)

[Constituency Tree Representation for Argument Unit Recognition](https://aclanthology.org/2023.argmining-1.4) (Guilluy et al., ArgMining-WS 2023)

• Constituency Tree Representation for Argument Unit Recognition (Guilluy et al., ArgMining-WS 2023)

ACL

• Samuel Guilluy, Florian Mehats, and Billal Chouli. 2023. Constituency Tree Representation for Argument Unit Recognition. In Proceedings of the 10th Workshop on Argument Mining, pages 35–44, Singapore. Association for Computational Linguistics.