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        <title>Towards Unification of Discourse Annotation Frameworks</title>
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        <namePart type="given">Yingxue</namePart>
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            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Samuel</namePart>
            <namePart type="family">Louvan</namePart>
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        <name type="personal">
            <namePart type="given">Andrea</namePart>
            <namePart type="family">Madotto</namePart>
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    <abstract>Discourse information is difficult to represent and annotate. Among the major frameworks for annotating discourse information, RST, PDTB and SDRT are widely discussed and used, each having its own theoretical foundation and focus. Corpora annotated under different frameworks vary considerably. To make better use of the existing discourse corpora and achieve the possible synergy of different frameworks, it is worthwhile to investigate the systematic relations between different frameworks and devise methods of unifying the frameworks. Although the issue of framework unification has been a topic of discussion for a long time, there is currently no comprehensive approach which considers unifying both discourse structure and discourse relations and evaluates the unified framework intrinsically and extrinsically. We plan to use automatic means for the unification task and evaluate the result with structural complexity and downstream tasks. We will also explore the application of the unified framework in multi-task learning and graphical models.</abstract>
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    <identifier type="doi">10.18653/v1/2022.acl-srw.12</identifier>
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%0 Conference Proceedings
%T Towards Unification of Discourse Annotation Frameworks
%A Fu, Yingxue
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F fu-2022-towards
%X Discourse information is difficult to represent and annotate. Among the major frameworks for annotating discourse information, RST, PDTB and SDRT are widely discussed and used, each having its own theoretical foundation and focus. Corpora annotated under different frameworks vary considerably. To make better use of the existing discourse corpora and achieve the possible synergy of different frameworks, it is worthwhile to investigate the systematic relations between different frameworks and devise methods of unifying the frameworks. Although the issue of framework unification has been a topic of discussion for a long time, there is currently no comprehensive approach which considers unifying both discourse structure and discourse relations and evaluates the unified framework intrinsically and extrinsically. We plan to use automatic means for the unification task and evaluate the result with structural complexity and downstream tasks. We will also explore the application of the unified framework in multi-task learning and graphical models.
%R 10.18653/v1/2022.acl-srw.12
%U https://aclanthology.org/2022.acl-srw.12
%U https://doi.org/10.18653/v1/2022.acl-srw.12
%P 132-142

Markdown (Informal)

[Towards Unification of Discourse Annotation Frameworks](https://aclanthology.org/2022.acl-srw.12) (Fu, ACL 2022)

• Towards Unification of Discourse Annotation Frameworks (Fu, ACL 2022)

ACL

• Yingxue Fu. 2022. Towards Unification of Discourse Annotation Frameworks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 132–142, Dublin, Ireland. Association for Computational Linguistics.