---
title: "discopy: A Neural System for Shallow — Knaebel 2021"
description: "Shallow discourse parsing (SDP) finds discourse relations in text."
authors: ["René Knaebel"]
year: 2021
venue: "Proceedings of the 2nd Workshop on Computational Approaches to Discourse"
source: "https://aclanthology.org/2021.codi-main.12/"
doi: "10.18653/v1/2021.codi-main.12"
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/knaebel-2021-discopy-neural-system-shallow-discourse/"
---
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# discopy: A Neural System for Shallow Discourse Parsing

**Authors:** [René Knaebel](https://md.research.elamur.ai/free/authors/rene-knaebel/) · **Year:** 2021 · **Venue:** Proceedings of the 2nd Workshop on Computational Approaches to Discourse · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.codi-main.12/) · [PDF](https://aclanthology.org/2021.codi-main.12.pdf) · [DOI 10.18653/v1/2021.codi-main.12](https://doi.org/10.18653/v1/2021.codi-main.12) · [OpenAlex](https://openalex.org/W3213141892)

**Citations:** 9 citations (openalex-snapshot · 2026-10-14) · [14](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Shallow discourse parsing (SDP) finds discourse relations in text. Each relation has two arguments and a sense, and it is either explicit (signalled by a connective) or non-explicit. The paper presents **discopy**, an open-source Python framework for building end-to-end SDP pipelines. Each component follows a shared interface (fit, parse, eval) and adds its predictions to the document as it moves through the pipeline, so parts can be swapped easily. The demonstration pipeline is fully neural and uses no hand-crafted linguistic features. Its components cover connective detection with sense classification, explicit argument extraction, and implicit/EntRel/NoRel classification. All of them share one set of precomputed contextualized embeddings from BERT-family models. Evaluation follows the CoNLL 2016 Shared Task setup on PDTB Section 23 and the Wikipedia blind test. Compared with the top 2016 systems (ecnucs, oslopots), the parser is better on most explicit-relation measures but clearly weaker on implicit argument spans. A FastAPI/VueJS web front end lets users inspect parser output. This gives SDP a reusable, modern toolkit with released pretrained models.

## Key points
- Code, documentation and pretrained models are available at github.com/rknaebel/discopy. Embeddings are built with Hugging Face transformers by concatenating the last four hidden layers for each token's first sub-token.
- Six encoders were tested: bert-base/large, roberta-base/large and albert-base/large. Base models generally beat large ones, and albert-base did well despite having the fewest parameters.
- Explicit connective F1 (strict, WSJ) reached up to 96.87 with albert-base, against 93.96 for ecnucs and 94.43 for oslopots. bert-base reached an explicit Arg1+Arg2 F1 of 51.79, against 44.31 for ecnucs.
- Explicit Arg1 identification stays slightly behind the baselines. Implicit argument extraction uses a simple adjacent-sentence heuristic and scores much lower (about 39–40 strict Arg1+Arg2 F1 on WSJ vs. 50.83–53.47 for the baselines).
- Results are reported under strict (exact match) and partial (≥70% overlap) evaluation. The web interface highlights arguments and connectives and can connect to a parser via REST API.

*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

14 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 12 not yet.

- [Investigating Explicitation of Discourse Connectives in Translation using Automatic Annotations](https://md.research.elamur.ai/free/papers/yung-2023-investigating-explicitation-discourse-connectives-translation/)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://md.research.elamur.ai/free/papers/zeldes-2025-erst-signaled-graph-theory-discourse/)

## Cite

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

```bibtex
@inproceedings{knaebel2021discopy,
  title = {{discopy: A Neural System for Shallow Discourse Parsing}},
  author = {Knaebel, René},
  booktitle = {Proceedings of the 2nd Workshop on Computational Approaches to Discourse},
  year = {2021},
  doi = {10.18653/v1/2021.codi-main.12},
  url = {https://doi.org/10.18653/v1/2021.codi-main.12}
}
```

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## References

21 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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