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    <titleInfo>
        <title>Tree Representations in Transition System for RST Parsing</title>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
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    <abstract>The transition-based systems in the past studies propose a series of actions, to build a right-heavy binarized tree for the RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.593</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Tree Representations in Transition System for RST Parsing
%A Li, Jinfen
%A Xiao, Lu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-xiao-2020-tree
%X The transition-based systems in the past studies propose a series of actions, to build a right-heavy binarized tree for the RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.
%R 10.18653/v1/2020.coling-main.593
%U https://aclanthology.org/2020.coling-main.593
%U https://doi.org/10.18653/v1/2020.coling-main.593
%P 6746-6751

Markdown (Informal)

[Tree Representations in Transition System for RST Parsing](https://aclanthology.org/2020.coling-main.593) (Li & Xiao, COLING 2020)

• Tree Representations in Transition System for RST Parsing (Li & Xiao, COLING 2020)

ACL

• Jinfen Li and Lu Xiao. 2020. Tree Representations in Transition System for RST Parsing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6746–6751, Barcelona, Spain (Online). International Committee on Computational Linguistics.