---
title: "Improving Implicit Discourse Relation — Dai 2018"
description: "Implicit discourse relations, which have no connective such as \"because\", are hard to classify."
authors: ["Zeyu Dai", "Ruihong Huang"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)"
source: "https://aclanthology.org/N18-1013/"
doi: "10.18653/v1/n18-1013"
arxiv: "1804.05918"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/dai-2018-improving-implicit-discourse-relation-classification/"
---
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# Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

**Authors:** [Zeyu Dai](https://md.research.elamur.ai/free/authors/zeyu-dai/), [Ruihong Huang](https://md.research.elamur.ai/free/authors/ruihong-huang/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-1013/) · [PDF](https://aclanthology.org/N18-1013.pdf) · [DOI 10.18653/v1/n18-1013](https://doi.org/10.18653/v1/n18-1013) · [arXiv 1804.05918](https://arxiv.org/abs/1804.05918) · [OpenAlex](https://openalex.org/W2963209355)

**Citations:** 64 citations (openalex-snapshot · 2026-10-14) · [31](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Implicit discourse relations, which have no connective such as "because", are hard to classify. Most earlier systems examine only the two discourse units (DUs) involved and ignore the rest of the paragraph. Dai and Huang propose a paragraph-level neural model for the PDTB corpus. It encodes a whole paragraph at once and labels every relation between adjacent DUs, both implicit and explicit.

The architecture has two levels:
- A word-level Bi-LSTM, followed by max-pooling, produces one vector per DU.
- A second, DU-level Bi-LSTM lets each unit's representation absorb information from its neighbours.

Two additions improve results further. Separate (untied) prediction layers handle explicit and implicit relations, and a CRF layer captures typical sequences of relation labels. The full model reaches 48.82% macro-F1 on four-way implicit classification, beating the strongest prior system. Explicit relations also improve slightly.

The work shows that wider context and the paragraph's overall discourse structure help recognize difficult implicit relations. Gains appear even for short paragraphs.

## Key points
- Input embeddings are 343-dimensional: 300-d Google word2vec, 36-d one-hot POS tags and 7-d named entity tags, all produced with Stanford CoreNLP.
- On implicit relations, the full model's 48.82% macro-F1 beats Lan et al. (2017) by about 1 point and neural tensor models such as Lei et al. (2017) by over 2 points.
- In binary classification, the model with untied parameters gains about 6 F1 points on Comparison and 7 on Temporal over the best previous systems.
- Majority-vote ensembles of 10 runs raise macro-F1 to 51.84% for implicit and 94.17% for explicit relations.
- Paragraph-level variants beat DU-pair baselines across all paragraph lengths. Gains are largest for paragraphs with more than 5 DUs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/)

## Cited by

Cited by

31 papers of our library cite it (of the 140673 whose bibliographies we read); 17 of them have a page here, 14 not yet.

- [Where Are We in Discourse Relation Recognition?](https://md.research.elamur.ai/free/papers/atwell-2021-where-are-we-discourse-relation/)
- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing](https://md.research.elamur.ai/free/papers/dai-2019-regularization-approach-incorporating-event-knowledge/)
- [Coreference for Discourse Parsing: A Neural Approach](https://md.research.elamur.ai/free/papers/guz-2020-coreference-discourse-parsing-neural-approach/)
- [TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/he-2020-transs-driven-joint-learning-architecture/)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/jiang-2023-global-local-hierarchy-aware-contrastive/)
- [Implicit Discourse Relation Classification: We Need to Talk about Evaluation](https://md.research.elamur.ai/free/papers/kim-2020-implicit-discourse-relation-classification-we/)
- [Zero-shot transfer for implicit discourse relation classification](https://md.research.elamur.ai/free/papers/kurfal-2019-zero-shot-transfer-implicit-discourse/)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://md.research.elamur.ai/free/papers/long-2022-facilitating-contrastive-learning-discourse-relational/)
- [Implicit Discourse Relation Identification for Open-domain Dialogues](https://md.research.elamur.ai/free/papers/ma-2019-implicit-discourse-relation-identification-open/)
- [Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings](https://md.research.elamur.ai/free/papers/nguyen-2019-employing-correspondence-relations-connectives-identify/)
- [Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks](https://md.research.elamur.ai/free/papers/varia-2019-discourse-relation-prediction-revisiting-word/)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://md.research.elamur.ai/free/papers/zeldes-2025-erst-signaled-graph-theory-discourse/)
- [Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhang-2021-context-tracking-network-graph-based/)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://md.research.elamur.ai/free/papers/zhao-2021-revisiting-shallow-discourse-parsing-pdtb/)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/)
- [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2022-prompt-based-connective-prediction-method/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dai2018improving,
  title = {{Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph}},
  author = {Dai, Zeyu and Huang, Ruihong},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1013},
  eprint = {1804.05918},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-1013}
}
```

- [← previous Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures](https://md.research.elamur.ai/free/papers/choubey-2018-improving-event-coreference-resolution-modeling/)
- [next → Improving Text-to-SQL Evaluation Methodology](https://md.research.elamur.ai/free/papers/dollak-2018-improving-text-sql-evaluation-methodology/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/papers/2018/page/2/)

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