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doi:10.22028/D291-30792
Title: | Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification |
Author(s): | Shi, Wei Demberg, Vera |
Editor(s): | Dobnik, Simon |
Language: | English |
Title: | Proceedings of the 13th International Conference on Computational Semantics - long papers : 23-27 May, 2019, University of Gothenburg, Gothenburg, Sweden : IWCS 2019 |
Startpage: | 188 |
Endpage: | 199 |
Publisher/Platform: | ACL |
Year of Publication: | 2019 |
Place of publication: | Stroudsburg |
Title of the Conference: | IWCS 2019 |
Place of the conference: | Gothenburg, Sweden |
Publikation type: | Conference Paper |
Abstract: | Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation. |
DOI of the first publication: | 10.18653/v1/W19-0416 |
URL of the first publication: | https://www.aclweb.org/anthology/W19-0416/ |
Link to this record: | hdl:20.500.11880/29706 http://dx.doi.org/10.22028/D291-30792 |
ISBN: | 978-1-950737-19-2 |
Date of registration: | 23-Sep-2020 |
Faculty: | MI - Fakultät für Mathematik und Informatik |
Department: | MI - Informatik |
Professorship: | MI - Prof. Dr. Vera Demberg |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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