# Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition

arXiv: https://arxiv.org/abs/2409.13716 · HTML: https://mlsanity.com/paper/2409.13716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification](https://mlsanity.com/paper/2109.05427/) (2021)
- [On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification](https://mlsanity.com/paper/2004.12617/) (2020)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [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)
- [A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2112.11740/) (2021)
- [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2210.07032/) (2022)
- [Label Anchored Contrastive Learning for Language Understanding](https://mlsanity.com/paper/2205.10227/) (2022)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022)

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

- [CLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification](https://mlsanity.com/paper/2509.16903/) (2025)

## Abstract (arXiv)

Previous approaches to the task of implicit discourse relation recognition (IDRR) generally view it as a classification task. Even with pre-trained language models, like BERT and RoBERTa, IDRR still relies on complicated neural networks with multiple intermediate layers to proper capture the interaction between two discourse units. As a result, the outputs of these intermediate layers may have different capability in discriminating instances of different classes. To this end, we propose to adapt a supervised contrastive learning (CL) method, label- and instance-centered CL, to enhance representation learning. Moreover, we propose a novel constrained multi-layer CL approach to properly impose a constraint that the contrastive loss of higher layers should be smaller than that of lower layers. Experimental results on PDTB 2.0 and PDTB 3.0 show that our approach can significantly improve the performance on both multi-class classification and binary classification.