# Multi-Label Classification for Implicit Discourse Relation Recognition

arXiv: https://arxiv.org/abs/2406.04461 · HTML: https://mlsanity.com/paper/2406.04461/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://mlsanity.com/paper/2206.02280/) (2022)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://mlsanity.com/paper/2010.02815/) (2020)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://mlsanity.com/paper/2301.02724/) (2023)
- [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/1807.05154/) (2018)
- [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning](https://mlsanity.com/paper/2106.03103/) (2021)
- [From Discourse to Narrative: Knowledge Projection for Event Relation Extraction](https://mlsanity.com/paper/2106.08629/) (2021)
- [Order-free Learning Alleviating Exposure Bias in Multi-label Classification](https://mlsanity.com/paper/1909.03434/) (2019)

## Built on by (most cited first, 9 of 9)

- [A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2408.08971/) (2024)
- [Discursive Circuits: How Do Language Models Understand Discourse Relations?](https://mlsanity.com/paper/2510.11210/) (2025)
- [Fine-Grained Evaluation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.05326/) (2025)
- [Evaluating Discourse Cohesion in Pre-trained Language Models](https://mlsanity.com/paper/2503.06137/) (2025)
- [Multi-Lingual Implicit Discourse Relation Recognition with Multi-Label Hierarchical Learning](https://mlsanity.com/paper/2508.20712/) (2025)
- [Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning](https://mlsanity.com/paper/2409.19075/) (2024)
- [Similarity-Based Domain Adaptation with LLMs](https://mlsanity.com/paper/2503.05281/) (2025)
- [MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning](https://mlsanity.com/paper/2503.06531/) (2025)
- [Human Label Variation in Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2602.22723/) (2026)

## Abstract (arXiv)

Discourse relations play a pivotal role in establishing coherence within textual content, uniting sentences and clauses into a cohesive narrative. The Penn Discourse Treebank (PDTB) stands as one of the most extensively utilized datasets in this domain. In PDTB-3, the annotators can assign multiple labels to an example, when they believe that multiple relations are present. Prior research in discourse relation recognition has treated these instances as separate examples during training, and only one example needs to have its label predicted correctly for the instance to be judged as correct. However, this approach is inadequate, as it fails to account for the interdependence of labels in real-world contexts and to distinguish between cases where only one sense relation holds and cases where multiple relations hold simultaneously. In our work, we address this challenge by exploring various multi-label classification frameworks to handle implicit discourse relation recognition. We show that multi-label classification methods don't depress performance for single-label prediction. Additionally, we give comprehensive analysis of results and data. Our work contributes to advancing the understanding and application of discourse relations and provide a foundation for the future study