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CODRA: A Novel Discriminative Framework for Rhetorical Analysis

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/J15-3002.pdf

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 · Natural language processing

Topics: Discourse analysis

Cited by

82 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 80 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00226

@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}
}

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