---
title: "The UniTN Discourse Parser in CoNLL 2015 — Stepanov 2015"
description: "The paper presents the University of Trento (UniTN) system submitted to the 2015 CoNLL shared task, which focused on shallow parsing of discourse."
authors: ["Evgeny Stepanov", "Giuseppe Riccardi", "Ali Orkan Bayer"]
year: 2015
venue: "Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task"
source: "https://aclanthology.org/K15-2003/"
doi: "10.18653/v1/k15-2003"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/stepanov-2015-unitn-discourse-parser-conll-2015/"
---
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# The UniTN Discourse Parser in CoNLL 2015 Shared Task: Token-level Sequence Labeling with Argument-specific Models

**Authors:** [Evgeny Stepanov](https://md.research.elamur.ai/free/authors/evgeny-stepanov/), [Giuseppe Riccardi](https://md.research.elamur.ai/free/authors/giuseppe-riccardi/), Ali Orkan Bayer · **Year:** 2015 · **Venue:** Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/K15-2003/) · [PDF](https://aclanthology.org/K15-2003.pdf) · [DOI 10.18653/v1/k15-2003](https://doi.org/10.18653/v1/k15-2003) · [OpenAlex](https://openalex.org/W2252250760)

**Citations:** 15 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

The paper presents the University of Trento (UniTN) system submitted to the 2015 CoNLL shared task, which focused on shallow parsing of discourse. The task is to find Penn Discourse Treebank (PDTB) style relations end to end: connectives, Argument 1 and Argument 2 spans, and senses, for both explicit and non-explicit (Implicit, AltLex, EntRel) relations. The authors build a pipeline that mirrors the PDTB annotation process. Span-finding steps for explicit relations, connective detection and argument extraction, are modeled as token-level sequence labeling with CRFs (CRF++). The argument models are specialized: separate models handle arguments of same-sentence (SS) and previous-sentence (PS) relations, and the SS models run in a cascade with Arg2 before Arg1. All other decisions use AdaBoost classifiers (icsiboost) with lexical, syntactic, dependency and Brown Cluster features. These decisions are connective sense, argument position, non-explicit relation detection, and sense. In the closed track, the parser placed second on the blind test set with a global F-measure of 0.2184. This suggests that token-level labeling is a workable strategy for explicit argument extraction.

## Key points

- The system was trained and evaluated on the PDTB data supplied by the organizers, including POS tags and constituency and dependency parses. Testing used WSJ Section 22 (dev), WSJ Section 23 (test), and a separately annotated blind test set.
- On the blind test set, the parser reached a global F1 of 0.2184 (P 0.2094, R 0.2283), ranking 2nd. The top system, lan15, scored 0.2400, and the third-place li15b scored 0.1851. The authors note that their parser tends to have higher recall than other entries.
- Explicit relations were handled much better than non-explicit ones (blind parser-level F1 0.2997 vs 0.1577). Non-explicit sense classification was the weakest component, with a macro-average of about 0.07.
- Argument position classification reached 0.9868 accuracy on dev when given gold connectives. The PS Arg1 step relies on the Previous Sentence Heuristic, even though about 9% of PS Arg1s are not in the adjacent sentence.
- Connective detection scored 0.8992 F1 on the blind set but ranked only 4th among participants. Because it is the first stage of the pipeline, the authors identify its robustness as a priority for improvement.

*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

10 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 6 not yet.

- [Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings](https://md.research.elamur.ai/free/papers/mihaylov-2016-discourse-relation-sense-classification-cross/)
- [Shallow Discourse Parsing Using Convolutional Neural Network](https://md.research.elamur.ai/free/papers/qin-2016-shallow-discourse-parsing-convolutional-neural/)
- [UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task](https://md.research.elamur.ai/free/papers/stepanov-2016-unitn-end-end-discourse-parser/)
- [The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/xue-2015-conll-2015-shared-task-shallow/)

## Cite

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

```bibtex
@inproceedings{stepanov2015unitn,
  title = {{The UniTN Discourse Parser in CoNLL 2015 Shared Task: Token-level Sequence Labeling with Argument-specific Models}},
  author = {Stepanov, Evgeny and Riccardi, Giuseppe and Bayer, Ali Orkan},
  booktitle = {Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task},
  year = {2015},
  doi = {10.18653/v1/k15-2003},
  url = {https://doi.org/10.18653/v1/k15-2003}
}
```

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## References

17 references, 8 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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