Labeling Explicit Discourse Relations using Pre-trained Language Models
This addresses a challenging NLP sub-task for discourse parsing, offering a novel approach that outperforms previous knowledge-intensive models.
The paper tackled labeling explicit discourse relations by using pre-trained language models, achieving state-of-the-art results on PDTB 2.0 without linguistic features.
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 the extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features.