Towards unification of discourse annotation frameworks

Yingxue Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationThe 60th annual meeting of the Association for Computational Linguistics
Subtitle of host publicationproceedings of the student research workshop
EditorsSamuel Louvan, Andrea Madotto, Brielen Madureira
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Number of pages11
ISBN (Print)9781955917230
Publication statusPublished - 25 May 2022
Event60th Annual Meeting of the Association for Computational Linguistics (ACL 2022): Student Research Workshop - Dublin, Ireland
Duration: 22 May 202227 May 2022
Conference number: 60


Workshop60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)
Abbreviated titleACL 2022
Internet address


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