# On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

arXiv: https://arxiv.org/abs/2004.12617 · HTML: https://mlsanity.com/paper/2004.12617/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 30 of 40)

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- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://mlsanity.com/paper/2306.06480/) (2023)
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- [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021)
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- [Exploring Discourse Structures for Argument Impact Classification](https://mlsanity.com/paper/2106.00976/) (2021)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022)
- [Multi-Granularity Optimization for Non-Autoregressive Translation](https://mlsanity.com/paper/2210.11017/) (2022)
- [Implicit Discourse Relation Classification For Nigerian Pidgin](https://mlsanity.com/paper/2406.18776/) (2024)
- [Video-Guided Curriculum Learning for Spoken Video Grounding](https://mlsanity.com/paper/2209.00277/) (2022)
- [Leveraging Hierarchical Prototypes as the Verbalizer for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2411.14880/) (2024)
- [A Novel Computational and Modeling Foundation for Automatic Coherence Assessment](https://mlsanity.com/paper/2310.00598/) (2023)
- [A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2408.08971/) (2024)
- [DePA: Improving Non-autoregressive Machine Translation with Dependency-Aware Decoder](https://mlsanity.com/paper/2203.16266/) (2022)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.10866/) (2023)
- [Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations](https://mlsanity.com/paper/2306.10658/) (2023)
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## Abstract (arXiv)

Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level. Previous studies mainly focus on the interactions between two arguments. We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together. Extensive experiments show that our proposed model outperforms BERT and other state-of-the-art systems on the PDTB dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the effectiveness of different modules in the implicit discourse relation classification task and demonstrate how different levels of representation learning can affect the results.