# Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction

arXiv: https://arxiv.org/abs/2106.03192 · HTML: https://mlsanity.com/paper/2106.03192/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 6 Jun 2021.

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

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/1807.05154/) (2018)
- [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1704.00217/) (2017)
- [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1811.01697/) (2018)
- [Implicit Discourse Relation Classification via Multi-Task Neural Networks](https://mlsanity.com/paper/1603.02776/) (2016)
- [Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification](https://mlsanity.com/paper/2003.02244/) (2020)

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

- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.03973/) (2023)
- [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)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://mlsanity.com/paper/2404.00999/) (2024)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [Prompting Implicit Discourse Relation Annotation](https://mlsanity.com/paper/2402.04918/) (2024)
- [Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.20588/) (2025)
- [A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification](https://mlsanity.com/paper/2307.03378/) (2023)
- [Multilingual Extraction and Recognition of Implicit Discourse Relations in Speech and Text](https://mlsanity.com/paper/2602.05107/) (2026)

## Abstract (arXiv)

In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approaches. In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation. We sidestep the lack of data through explicitation of implicit relations to reduce the task to two sub-problems: language modeling and explicit discourse relation classification, a much easier problem. Our experimental results show that this method can even marginally outperform the state-of-the-art, in spite of being much simpler than alternative models of comparable performance. Moreover, we show that the achieved performance is robust across domains as suggested by the zero-shot experiments on a completely different domain. This indicates that recent advances in language modeling have made language models sufficiently good at capturing inter-sentence relations without the help of explicit discourse markers.