---
title: "CODRA: A Novel Discriminative Framework for — Joty 2015"
description: "The paper addresses automatic rhetorical analysis under Rhetorical Structure Theory (RST), where a text is represented as a labeled discourse tree (DT)…"
authors: ["Shafiq Joty", "Giuseppe Carenini", "Raymond T. Ng"]
year: 2015
venue: "Computational Linguistics"
source: "https://aclanthology.org/J15-3002/"
doi: "10.1162/coli_a_00226"
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/en/papers/joty-2015-codra-novel-discriminative-framework-rhetorical/"
---
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# CODRA: A Novel Discriminative Framework for Rhetorical Analysis

**Authors:** [Shafiq Joty](https://md.research.elamur.ai/free/en/authors/shafiq-joty/), [Giuseppe Carenini](https://md.research.elamur.ai/free/en/authors/giuseppe-carenini/), Raymond T. Ng · **Year:** 2015 · **Venue:** Computational Linguistics · **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/J15-3002/) · [PDF](https://aclanthology.org/J15-3002.pdf) · [DOI 10.1162/coli\_a\_00226](https://doi.org/10.1162/coli_a_00226) · [OpenAlex](https://openalex.org/W1894075015)

**Citations:** 181 citations (openalex-snapshot · 2026-10-14) · [82](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses automatic rhetorical analysis under Rhetorical Structure Theory (RST), where a text is represented as a labeled discourse tree (DT) over clause-like elementary discourse units (EDUs). Earlier parsers had three weaknesses: they predicted tree structure and relation labels separately, built trees greedily, and treated sentence-internal and cross-sentence structure the same way.

The authors present CODRA, a complete pipeline. A discriminative binary classifier first segments the text into EDUs, using fewer features than earlier segmenters. Two Conditional Random Fields then score candidate tree constituents, jointly modeling structure and relation label: one CRF works within sentences and the other across sentences. A CKY-style bottom-up algorithm uses these probabilities to find the optimal tree and can also output k-best trees. The two parsing levels are combined in two ways: plain sentence-by-sentence parsing, or a two-sentence sliding window for "leaky" sentence boundaries.

Tested on news (RST–DT) and how-to manuals, CODRA significantly outperforms prior systems. Oracle k-best results suggest reranking could improve it further. This matters because more accurate discourse parsing makes RST practical for downstream applications.

## Key points
- RST–DT is built on Wall Street Journal texts from the Penn Treebank. It uses 53 mono-nuclear and 25 multi-nuclear relations, grouped into 16 coarse classes.
- The parser has separate intra-sentential and multi-sentential CRF models, decoded with an optimal CKY-like algorithm that can return k-best hypotheses.
- Two combination strategies are proposed: sentence-level parsing followed by document-level parsing, and a sliding window over adjacent sentence pairs.
- Evaluation covers two genres: news articles and instructional manuals. Previous parsers had been tested on only one corpus.
- The source code, an online demo and the evaluation metrics were released. The tool was later used in discourse-aware MT metrics that won the WMT 2014 metrics shared task.

*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/en/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

82 papers of our library cite it (of the 140673 whose bibliographies we read); 20 of them have a page here, 62 not yet.

- [Discourse parsing for multi-party chat dialogues](https://md.research.elamur.ai/free/en/papers/afantenos-2015-discourse-parsing-multi-party-chat/)
- [Detecting Subevents using Discourse and Narrative Features](https://md.research.elamur.ai/free/en/papers/aldawsari-2019-detecting-subevents-discourse-narrative-features/)
- [Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/)
- [The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bourgonje-2020-potsdam-commentary-corpus-2-2/)
- [Structured Dialogue Discourse Parsing](https://md.research.elamur.ai/free/en/papers/chi-2022-structured-dialogue-discourse-parsing/)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://md.research.elamur.ai/free/en/papers/chistova-2024-bilingual-rhetorical-structure-parsing-large/)
- [Coreference for Discourse Parsing: A Neural Approach](https://md.research.elamur.ai/free/en/papers/guz-2020-coreference-discourse-parsing-neural-approach/)
- [Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining](https://md.research.elamur.ai/free/en/papers/guz-2020-unleashing-power-neural-discourse-parsers/)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://md.research.elamur.ai/free/en/papers/he-2021-multi-tasking-dialogue-comprehension-discourse/)
- [MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision](https://md.research.elamur.ai/free/en/papers/huber-2020-mega-rst-discourse-treebanks-structure/)
- [Discourse Structure in Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/joty-2017-discourse-structure-machine-translation-evaluation/)
- [A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://md.research.elamur.ai/free/en/papers/kobayashi-2022-simple-strong-baseline-end-end/)
- [Top-down Discourse Parsing via Sequence Labelling](https://md.research.elamur.ai/free/en/papers/koto-2021-top-down-discourse-parsing-sequence/)
- [A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/lin-2019-unified-linear-time-framework-sentence/)
- [Neural Generative Rhetorical Structure Parsing](https://md.research.elamur.ai/free/en/papers/mabona-2019-neural-generative-rhetorical-structure-parsing/)
- [How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT](https://md.research.elamur.ai/free/en/papers/morey-2017-how-much-progress-have-we/)
- [A Dependency Perspective on RST Discourse Parsing and Evaluation](https://md.research.elamur.ai/free/en/papers/morey-2018-dependency-perspective-rst-discourse-parsing/)
- [Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/nejat-2017-exploring-joint-neural-model-sentence/)
- [Integer Linear Programming for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/perret-2016-integer-linear-programming-discourse-parsing/)
- [RST Discourse Parsing with Second-Stage EDU-Level Pre-training](https://md.research.elamur.ai/free/en/papers/yu-2022-rst-discourse-parsing-second-stage/)

## Cite

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

```bibtex
@article{joty2015codra,
  title = {{CODRA: A Novel Discriminative Framework for Rhetorical Analysis}},
  author = {Joty, Shafiq and Carenini, Giuseppe and Ng, Raymond T.},
  journal = {Computational Linguistics},
  year = {2015},
  doi = {10.1162/coli\_a\_00226},
  url = {https://doi.org/10.1162/coli_a_00226}
}
```

- [← previous CLPsych 2015 Shared Task: Depression and PTSD on Twitter](https://md.research.elamur.ai/free/en/papers/coppersmith-2015-clpsych-2015-shared-task-depression/)
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