# Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

arXiv: https://arxiv.org/abs/1609.06380 · HTML: https://mlsanity.com/paper/1609.06380/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 16 of 16)

- [Re-Reading Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2309.06275/) (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)
- [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)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2402.15080/) (2024)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022)
- [Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis](https://mlsanity.com/paper/2201.02026/) (2022)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://mlsanity.com/paper/2204.00350/) (2022)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.10866/) (2023)
- [Discourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems](https://mlsanity.com/paper/2602.23266/) (2026)
- [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2311.00367/) (2023)
- [Fine-Grained Evaluation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.05326/) (2025)
- [Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2309.07561/) (2023)

## Abstract (arXiv)

Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for recognizing discourse relations. To mimic the repeated reading strategy, we propose the neural networks with multi-level attention (NNMA), combining the attention mechanism and external memories to gradually fix the attention on some specific words helpful to judging the discourse relations. Experiments on the PDTB dataset show that our proposed method achieves the state-of-art results. The visualization of the attention weights also illustrates the progress that our model observes the arguments on each level and progressively locates the important words.