# Labeling Explicit Discourse Relations using Pre-trained Language Models

arXiv: https://arxiv.org/abs/2006.11852 · HTML: https://mlsanity.com/paper/2006.11852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Lightweight Connective Detection Using Gradient Boosting](https://mlsanity.com/paper/2404.13793/) (2024)

## Abstract (arXiv)

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.