---
title: "Constrained Decoding for Text-Level Discourse — Muller 2012"
description: "The paper addresses discourse parsing for whole documents rather than single sentences."
authors: ["Philippe Muller", "Stergos Afantenos", "Pascal Denis", "Nicholas Asher"]
year: 2012
venue: "Proceedings of COLING 2012"
source: "https://aclanthology.org/C12-1115/"
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/muller-2012-constrained-decoding-text-level-discourse/"
---
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# Constrained Decoding for Text-Level Discourse Parsing

**Authors:** [Philippe Muller](https://md.research.elamur.ai/free/authors/philippe-muller/), [Stergos Afantenos](https://md.research.elamur.ai/free/authors/stergos-afantenos/), [Pascal Denis](https://md.research.elamur.ai/free/authors/pascal-denis/), [Nicholas Asher](https://md.research.elamur.ai/free/authors/nicholas-asher/) · **Year:** 2012 · **Venue:** Proceedings of COLING 2012 · **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/C12-1115/) · [PDF](https://aclanthology.org/C12-1115.pdf) · [OpenAlex](https://openalex.org/W2097700060)

**Citations:** 59 citations (openalex-snapshot · 2026-10-14) · [40](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses discourse parsing for whole documents rather than single sentences. Earlier full-text parsers built structures greedily from independent local decisions. The authors train local classifiers on pairs of discourse units: one predicts whether two units are attached, and another predicts the coherence relation between them. They then decode a globally optimal structure from these scores. Discourse structures are represented as dependency-style graphs, which abstract over frameworks such as SDRT and RST. Decoding uses A* search, which can enforce linguistic constraints like the Right Frontier Constraint, and is compared with Maximum Spanning Tree (MST) and greedy decoding. Attachment and labeling are tried both jointly and as a pipeline. Experiments use the French Annodis corpus, making this the first discourse parser for French. Despite far less training data than English systems, it reaches 66.2 F1 on unlabeled and 46.8 F1 on labeled structures. These scores are comparable to prior work and show that constrained global decoding is a practical alternative to greedy parsing.

## Key points
- Data: Annodis corpus (French, SDRT-based). Training contains about 2.5k positive attachments out of 13k pairs with pruning, and about 2.7k out of 125k without pruning.
- Results: unlabeled F1 is 66.2. Labeled F1 is 46.8 with 4 coarse relations and 36.1 with 18 relations. MST and A* decoding performed best.
- For labeled structures, pipelining relation labeling after attachment beat joint decoding. Pruning the attachment space to a window of 5 units gave no accuracy gain.
- Comparison with related work: Hernault et al. (2010) report 68.4 F1 on RST structure, and Feng and Hirst (2012) report 69.84 F1 on attachment. Baldridge and Lascarides (2005) report 68/43 on Verbmobil dialogues. The proposed system lands in a similar range with fewer features and much less data.
- Planned future work includes better relation models, handling intra-sentential links separately, adapting the approach to RST corpora, integrating decoding into learning, and k-best parsing.

*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

40 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 28 not yet.

- [Discourse parsing for multi-party chat dialogues](https://md.research.elamur.ai/free/papers/afantenos-2015-discourse-parsing-multi-party-chat/)
- [Multi-view and multi-task training of RST discourse parsers](https://md.research.elamur.ai/free/papers/braud-2016-multi-view-multi-task-training/)
- [The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.research.elamur.ai/free/papers/braud-2023-disrpt-2023-shared-task-elementary/)
- [A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing](https://md.research.elamur.ai/free/papers/fan-2022-distance-aware-multi-task-framework/)
- [A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing](https://md.research.elamur.ai/free/papers/feng-2014-linear-time-bottom-up-discourse/)
- [Representation Learning for Text-level Discourse Parsing](https://md.research.elamur.ai/free/papers/ji-2014-representation-learning-text-level-discourse/)
- [Dialogue Discourse Parsing as Generation: A Sequence-to-Sequence LLM-based Approach](https://md.research.elamur.ai/free/papers/li-2024-dialogue-discourse-parsing-as-generation/)
- [A Dependency Perspective on RST Discourse Parsing and Evaluation](https://md.research.elamur.ai/free/papers/morey-2018-dependency-perspective-rst-discourse-parsing/)
- [Joint prediction in MST-style discourse parsing for argumentation mining](https://md.research.elamur.ai/free/papers/peldszus-2015-joint-prediction-mst-style-discourse/)
- [Integer Linear Programming for Discourse Parsing](https://md.research.elamur.ai/free/papers/perret-2016-integer-linear-programming-discourse-parsing/)
- [A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech](https://md.research.elamur.ai/free/papers/yang-2021-joint-model-dropped-pronoun-recovery/)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://md.research.elamur.ai/free/papers/zhao-2021-revisiting-shallow-discourse-parsing-pdtb/)

## Cite

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

```bibtex
@inproceedings{muller2012constrained,
  title = {{Constrained Decoding for Text-Level Discourse Parsing}},
  author = {Muller, Philippe and Afantenos, Stergos and Denis, Pascal and Asher, Nicholas},
  booktitle = {Proceedings of COLING 2012},
  year = {2012},
  url = {https://aclanthology.org/C12-1115/}
}
```

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