Labeling explicit discourse relations is one of the most challenging sub-tasks of the shallow discourse parsing where the goal is to identify the discourse connectives and the boundaries of their arguments. The state-of-the-art models achieve slightly above 45% of F-score by using hand-crafted features. The current paper investigates the efficacy of the pre-trained language models in this task. We find that the pre-trained language models, when finetuned, are powerful enough to replace the linguistic features. We evaluate our model on PDTB 2.0 and report the state-of-the-art results in extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features.
CITATION STYLE
Kurfalı, M. (2020). Labeling explicit discourse relations using pre-trained language models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12284 LNAI, pp. 79–86). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58323-1_8
Mendeley helps you to discover research relevant for your work.