# Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition

arXiv: https://arxiv.org/abs/2210.07032 · HTML: https://mlsanity.com/paper/2210.07032/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [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)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [PromptBERT: Improving BERT Sentence Embeddings with Prompts](https://mlsanity.com/paper/2201.04337/) (2022)
- [AdaPrompt: Adaptive Model Training for Prompt-based NLP](https://mlsanity.com/paper/2202.04824/) (2022)
- [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)
- [Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph](https://mlsanity.com/paper/1804.05918/) (2018)
- [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://mlsanity.com/paper/1609.06380/) (2016)
- [Modeling Coherence for Discourse Neural Machine Translation](https://mlsanity.com/paper/1811.05683/) (2018)
- [Implicit Discourse Relation Classification via Multi-Task Neural Networks](https://mlsanity.com/paper/1603.02776/) (2016)
- [Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension](https://mlsanity.com/paper/1908.10721/) (2019)
- [Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction](https://mlsanity.com/paper/2106.03192/) (2021)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)

## Built on by (most cited first, 14 of 14)

- [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)
- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://mlsanity.com/paper/2306.06480/) (2023)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2402.15080/) (2024)
- [Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning](https://mlsanity.com/paper/2408.11599/) (2024)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://mlsanity.com/paper/2404.00999/) (2024)
- [A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2408.08971/) (2024)
- [Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations](https://mlsanity.com/paper/2306.10658/) (2023)
- [Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues](https://mlsanity.com/paper/2503.03474/) (2025)
- [Prompting Implicit Discourse Relation Annotation](https://mlsanity.com/paper/2402.04918/) (2024)
- [Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition](https://mlsanity.com/paper/2409.13716/) (2024)
- [Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.20588/) (2025)
- [On the Role of Context for Discourse Relation Classification in Scientific Writing](https://mlsanity.com/paper/2510.26354/) (2025)
- [Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs](https://mlsanity.com/paper/2602.21763/) (2026)

## Abstract (arXiv)

Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted multi-task learning to aid IDRR through explicit discourse relation recognition (EDRR) or utilized dependencies between discourse relation labels to constrain model predictions. But these methods still performed poorly on fine-grained IDRR and even utterly misidentified on most of the few-shot discourse relation classes. To address these problems, we propose a novel Prompt-based Connective Prediction (PCP) method for IDRR. Our method instructs large-scale pre-trained models to use knowledge relevant to discourse relation and utilizes the strong correlation between connectives and discourse relation to help the model recognize implicit discourse relations. Experimental results show that our method surpasses the current state-of-the-art model and achieves significant improvements on those fine-grained few-shot discourse relation. Moreover, our approach is able to be transferred to EDRR and obtain acceptable results. Our code is released in https://github.com/zh-i9/PCP-for-IDRR.