# A Survey of Implicit Discourse Relation Recognition arXiv: https://arxiv.org/abs/2203.02982 ยท HTML: https://mlsanity.com/paper/2203.02982/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 42 of its 222 references that are in the map. - First posted on arXiv 6 Mar 2022. ## Builds on (most cited first, 30 of 42) - [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) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014) - [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016) - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018) - [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014) - [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015) - [On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification](https://mlsanity.com/paper/2004.12617/) (2020) - [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018) - [Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation](https://mlsanity.com/paper/1508.02096/) (2015) - [TransG : A Generative Mixture Model for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05488/) (2015) - [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) - [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1704.00217/) (2017) - [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) - [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1811.01697/) (2018) - [CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse Relations](https://mlsanity.com/paper/2012.15643/) (2020) - [Implicit Discourse Relation Classification via Multi-Task Neural Networks](https://mlsanity.com/paper/1603.02776/) (2016) - [Document Context Language Models](https://mlsanity.com/paper/1511.03962/) (2015) - [Extending Implicit Discourse Relation Recognition to the PDTB-3](https://mlsanity.com/paper/2010.06294/) (2020) - [Implicit Discourse Relation Identification for Open-domain Dialogues](https://mlsanity.com/paper/1907.03975/) (2019) - [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021) ## Built on by (most cited first, 8 of 8) - [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2210.07032/) (2022) - [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://mlsanity.com/paper/2306.06480/) (2023) - [Identifying while Learning for Document Event Causality Identification](https://mlsanity.com/paper/2405.20608/) (2024) - [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.10866/) (2023) - [Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments](https://mlsanity.com/paper/2312.03726/) (2023) - [Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2309.07561/) (2023) - [Multilingual Extraction and Recognition of Implicit Discourse Relations in Speech and Text](https://mlsanity.com/paper/2602.05107/) (2026) - [Human Label Variation in Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2602.22723/) (2026) ## Abstract (arXiv) A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.