---
title: "Multi-Label Classification for Implicit — Long 2024"
description: "In PDTB-3, annotators sometimes give one implicit discourse relation two senses at once, such as a temporal and a concessive reading of the same argument…"
authors: ["Wanqiu Long", "N. Siddharth", "Bonnie Webber"]
year: 2024
venue: "Findings of the Association for Computational Linguistics: ACL 2024"
source: "https://aclanthology.org/2024.findings-acl.500/"
doi: "10.18653/v1/2024.findings-acl.500"
arxiv: "2406.04461"
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/long-2024-multi-label-classification-implicit-discourse/"
---
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# Multi-Label Classification for Implicit Discourse Relation Recognition

**Authors:** [Wanqiu Long](https://md.research.elamur.ai/free/authors/wanqiu-long/), N. Siddharth, [Bonnie Webber](https://md.research.elamur.ai/free/authors/bonnie-webber/) · **Year:** 2024 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2024 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.findings-acl.500/) · [PDF](https://aclanthology.org/2024.findings-acl.500.pdf) · [DOI 10.18653/v1/2024.findings-acl.500](https://doi.org/10.18653/v1/2024.findings-acl.500) · [arXiv 2406.04461](https://arxiv.org/abs/2406.04461) · [OpenAlex](https://openalex.org/W4402670781)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · 6 in our library, of the 140673 papers whose bibliographies we read

## Summary
In PDTB-3, annotators sometimes give one implicit discourse relation two senses at once, such as a temporal and a concessive reading of the same argument pair. Earlier implicit discourse relation recognition (IDRR) systems split such cases into separate training items and counted a prediction as correct if it hit either gold sense. This ignores how co-occurring senses relate and cannot tell single-sense from multi-sense cases. The authors reframe IDRR as multi-label classification, which they describe as a first for this task. They compare three RoBERTa-based models:
- a single sigmoid head,
- separate per-label binary softmax heads,
- a GRU decoder that generates labels in sequence.

To cope with rare senses and sense pairs, they evaluate on Level-2 senses with section-level cross-validation instead of the usual fixed split. Multi-label training does not hurt single-label prediction and can slightly improve it, with the per-label-head model performing best. The paper also analyses errors and data sparsity, and argues that richer, more varied multi-sense corpora are needed.

## Key points
- Data: implicit relations in PDTB-3, both intra- and inter-sentential. Roughly 5% of them carry two sense labels.
- Evaluation uses 12 section-level folds (21 sections train, 2 dev, 2 test) and macro-averaged F1. Precision, Recall and Hamming loss are given in the appendix.
- Methods:
  - Method 1: one sigmoid BCE head on [CLS].
  - Method 2: per-label binary softmax heads.
  - Method 3: GRU decoder with dot-product attention and beam search (beam size 4).
  - All use RoBERTa with learning rate 1e−5 and batch size 64.
- Method 2 scores best, around 53.1 total macro-F1 versus about 52.4 for Method 1. The authors also report focal loss as a preferable loss for this task.
- Cause, Condition, Purpose and Conjunction are recognised reliably across methods. Cause+Belief (near 0 F1), Synchronous and Equivalence remain hard.

*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/)

## Cite

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

```bibtex
@inproceedings{long2024multi,
  title = {{Multi-Label Classification for Implicit Discourse Relation Recognition}},
  author = {Long, Wanqiu and Siddharth, N. and Webber, Bonnie},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-acl.500},
  eprint = {2406.04461},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2024.findings-acl.500}
}
```

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36. Comparison Contingency Expansion Temporal Comparison.Concession Comparison.Contrast Contingency.Cause Contingency.Cause+Belief Contingency.Condition Contingency.Purpose Expansion.Conjunction Expansion.Equivalence Expansion.Instantiation Expansion.Level-of-detail Expansion.Manner Expansion.Substituti… [find](https://scholar.google.com/scholar?q=Comparison+Contingency+Expansion+Temporal+Comparison%2EConcession+Comparison%2EContrast+Contingency%2ECause+Contingency%2ECause%2BBelief+Contingency%2ECondition+Contingency%2EPurpose+Expansion%2EConjunction+Expansion)
37. Asynchronous/Cause Asynchronous/Contrast Cause+Belief/Instantiation Cause+Belief/Level-of-detail Cause/Asynchronous Cause/Instantiation Cause/Level-of-detail Cause/Manner Cause/Substitution Concession/Asynchronous Concession/Substitution Condition/Manner Conjunction/Asynchronous Conjunction/Cause Co… [find](https://scholar.google.com/scholar?q=Asynchronous%2FCause+Asynchronous%2FContrast+Cause%2BBelief%2FInstantiation+Cause%2BBelief%2FLevel%2Dof%2Ddetail+Cause%2FAsynchronous+Cause%2FInstantiation+Cause%2FLevel%2Dof%2Ddetail+Cause%2FManner+Cause%2FSubstitution+Concession)