# Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition

arXiv: https://arxiv.org/abs/2311.00367 · HTML: https://mlsanity.com/paper/2311.00367/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://mlsanity.com/paper/2009.12061/) (2020)
- [A Mutual Information Maximization Perspective of Language Representation Learning](https://mlsanity.com/paper/1910.08350/) (2019)
- [On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification](https://mlsanity.com/paper/2004.12617/) (2020)
- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.03973/) (2023)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://mlsanity.com/paper/2301.02724/) (2023)
- [A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2112.11740/) (2021)
- [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://mlsanity.com/paper/1609.06380/) (2016)
- [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1811.01697/) (2018)
- [Contextual Representation Learning beyond Masked Language Modeling](https://mlsanity.com/paper/2204.04163/) (2022)
- [From Discourse to Narrative: Knowledge Projection for Event Relation Extraction](https://mlsanity.com/paper/2106.08629/) (2021)
- [Extending Implicit Discourse Relation Recognition to the PDTB-3](https://mlsanity.com/paper/2010.06294/) (2020)
- [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)

- [Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs](https://mlsanity.com/paper/2602.21763/) (2026)

## Abstract (arXiv)

Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit the hierarchical structure information from the annotated senses, which demonstrate enhanced discourse relation representations can be obtained by integrating sense hierarchy. Nevertheless, the performance and robustness for IDRR are significantly constrained by the availability of annotated data. Fortunately, there is a wealth of unannotated utterances with explicit connectives, that can be utilized to acquire enriched discourse relation features. In light of such motivation, we propose a Prompt-based Logical Semantics Enhancement (PLSE) method for IDRR. Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Furthermore, considering the prompt-based connective prediction exhibits local dependencies due to the deficiency of masked language model (MLM) in capturing global semantics, we design a novel self-supervised learning objective based on mutual information maximization to derive enhanced representations of logical semantics for IDRR. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.