---
title: "Not Just Classification — Jiang 2021"
description: "Implicit discourse relation recognition (IDRR) is usually treated as plain label classification, so models never see what a relation actually means."
authors: ["Feng Jiang", "Yaxin Fan", "Xiaomin Chu", "Peifeng Li", "Qiaoming Zhu"]
year: 2021
venue: "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2021.emnlp-main.187/"
doi: "10.18653/v1/2021.emnlp-main.187"
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/en/papers/jiang-2021-not-just-classification-recognizing-implicit/"
---
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# Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation

**Authors:** [Feng Jiang](https://md.research.elamur.ai/free/en/authors/feng-jiang/), [Yaxin Fan](https://md.research.elamur.ai/free/en/authors/yaxin-fan/), [Xiaomin Chu](https://md.research.elamur.ai/free/en/authors/xiaomin-chu/), [Peifeng Li](https://md.research.elamur.ai/free/en/authors/peifeng-li/), [Qiaoming Zhu](https://md.research.elamur.ai/free/en/authors/qiaoming-zhu/) · **Year:** 2021 · **Venue:** Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.emnlp-main.187/) · [PDF](https://aclanthology.org/2021.emnlp-main.187.pdf) · [DOI 10.18653/v1/2021.emnlp-main.187](https://doi.org/10.18653/v1/2021.emnlp-main.187) · [OpenAlex](https://openalex.org/W3211382138)

**Citations:** 14 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Implicit discourse relation recognition (IDRR) is usually treated as plain label classification, so models never see what a relation actually means. The authors also cast IDRR as text generation. Their joint model, CG-T5, built on T5, predicts a relation label from encoder states via a span representation layer while the decoder writes a natural-language target sentence expressing the relation. They test three target forms: the bare relation name, an explanation of the relation (placed before or after the name), and a question about one unit, an idea borrowed from QA-style annotation. Long discourse units, such as whole paragraphs, would not fit into such templates. A semantic role labeling–based step therefore compresses each unit into short core information. On the Chinese MCDTB and the English PDTB, CG-T5 beats several state-of-the-art baselines, and the question form works best. The classifier is stronger on frequent classes, while the generator handles rare ones better and gives readable output. Combining both improves accuracy and interpretability.

## Key points
- **Corpora:** MCDTB has 720 Xinhua News articles with paragraph-level relations (3 classes, 15 types), all treated as implicit. PDTB is evaluated with top-level 4-way classification.
- **PDTB per-class results:** The question-form generator reaches 55.40 F1 on Comparison, 8.21 points above BERT. The authors attribute this to cue words like "negate"/"opposite" in the question templates.
- **MCDTB fold 1, classifier output:** The joint classifier scores 62.75 micro-F1 at second level and 71.79 at top level.
- **MCDTB fold 1, agreement and oracle:** The classifier and generator agree on 91.34% (second level) and 93.51% (top level) of cases. An oracle choosing the correct one would reach 65.00 and 74.19.
- **Generation quality:** Rouge-1/2/3/L scores are 83.99/79.69/77.22/82.97, above both vanilla T5 and GPT-2. In ablations, joint training improved both the classification and generation heads.

*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/en/shelves/linguistics/)

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

## Cited by

Cited by

16 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 8 not yet.

- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://md.research.elamur.ai/free/en/papers/long-2022-facilitating-contrastive-learning-discourse-relational/)
- [Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models' Understanding of Discourse Relations](https://md.research.elamur.ai/free/en/papers/miao-2024-discursive-socratic-questioning-evaluating-faithfulness/)
- [An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/omura-2024-empirical-study-synthetic-data-generation/)
- [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/wang-2023-prompt-based-logical-semantics-enhancement/)
- [ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-connprompt-connective-cloze-prompt-learning/)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2023-teprompt-task-enlightenment-prompt-learning/)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/)

## Cite

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

```bibtex
@inproceedings{jiang2021not,
  title = {{Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation}},
  author = {Jiang, Feng and Fan, Yaxin and Chu, Xiaomin and Li, Peifeng and Zhu, Qiaoming},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  year = {2021},
  doi = {10.18653/v1/2021.emnlp-main.187},
  url = {https://doi.org/10.18653/v1/2021.emnlp-main.187}
}
```

- [← previous NOPE: A Corpus of Naturally-Occurring Presuppositions in English](https://md.research.elamur.ai/free/en/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/)
- [next → OCR-free Document Understanding Transformer](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/)

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