---
title: "Let's be explicit about that — Kurfalı 2021"
description: "Classifying implicit discourse relations, where adjacent sentences are linked without any overt connective, is hard."
authors: ["Murathan Kurfalı", "Robert Östling"]
year: 2021
venue: "Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language"
source: "https://aclanthology.org/2021.unimplicit-1.1/"
doi: "10.18653/v1/2021.unimplicit-1.1"
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/kurfal-2021-let-s-be-explicit-about/"
---
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# Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction

**Authors:** [Murathan Kurfalı](https://md.research.elamur.ai/free/authors/murathan-kurfal/), [Robert Östling](https://md.research.elamur.ai/free/authors/robert-ostling/) · **Year:** 2021 · **Venue:** Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.unimplicit-1.1/) · [PDF](https://aclanthology.org/2021.unimplicit-1.1.pdf) · [DOI 10.18653/v1/2021.unimplicit-1.1](https://doi.org/10.18653/v1/2021.unimplicit-1.1) · [OpenAlex](https://openalex.org/W3176670238)

**Citations:** 14 citations (openalex-snapshot · 2026-10-14) · [17](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Classifying implicit discourse relations, where adjacent sentences are linked without any overt connective, is hard. Even human annotators often disagree, so labeled data is scarce. Kurfalı and Östling avoid using any annotated implicit relations. Instead, they split the problem into language modeling and explicit relation classification, which is a much easier task. Their pipeline works in three steps:

- It inserts each of 65 single-word PDTB 2.0 connectives between the two arguments.
- It ranks the resulting candidates with an off-the-shelf pre-trained language model (BERT variants, RoBERTa, DistilBERT or GPT-2).
- It labels the candidates with a classifier trained only on PDTB explicit relations. The final sense comes either from the top-ranked connective or from marginalizing over all connectives.

Despite its simplicity, this distantly supervised setup slightly beats the previous best method that also uses no implicit labels, a more complex adversarial domain-adaptation model. It is also the first such work to report 11-way results. Without any tuning on biomedical text, it generalizes well to BioDRB, surpassing the zero-shot transfer of supervised systems. The work also offers connective explicitation as a probe of how well language models grasp relations between sentences.

## Key points
- **Motivation from an oracle experiment:** filling in the gold implicit connectives allowed 4-way classification at an F-score of 88.74. This shows how much is gained once a relation is made explicit.
- **Connective set:** of the 100 connectives in the PDTB 2.0 guidelines, only the 65 one-word items were used, because masked LMs cannot fill several tokens at once.
- **Classifier training data:** 13,639 PDTB explicit relations for 4-way and 12,695 for 11-way classification. Explicit relations not in arg1–connective–arg2 order were dropped, which removed 2,558 instances (13.85%).
- **Comparisons:** in-domain, the baseline is Huang and Li (2019). Cross-domain, results on BioDRB are compared with Shi and Demberg (2019b), on both the standard test split (247 instances for 4-way, 140 for 11-way) and the full corpus (3,001 and 1,755).
- **Why it transfers well:** the authors argue that LMs left unfine-tuned carry knowledge from many domains. Once these models surface a fitting connective, the explicit classifier is less sensitive to the shift in domain.

*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

17 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 10 not yet.

- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://md.research.elamur.ai/free/papers/liu-2023-annotation-inspired-implicit-discourse-relation/)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://md.research.elamur.ai/free/papers/liu-2024-what-causes-failure-explicit-implicit/)
- [Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models' Understanding of Discourse Relations](https://md.research.elamur.ai/free/papers/miao-2024-discursive-socratic-questioning-evaluating-faithfulness/)
- [Comparison of methods for explicit discourse connective identification across various domains](https://md.research.elamur.ai/free/papers/scholman-2021-comparison-methods-explicit-discourse-connective/)
- [Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation](https://md.research.elamur.ai/free/papers/wu-2023-connective-prediction-implicit-discourse-relation/)
- [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{kurfal2021let,
  title = {{Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction}},
  author = {Kurfalı, Murathan and Östling, Robert},
  booktitle = {Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language},
  year = {2021},
  doi = {10.18653/v1/2021.unimplicit-1.1},
  url = {https://doi.org/10.18653/v1/2021.unimplicit-1.1}
}
```

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## References

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