---
title: "Cross-lingual Discourse Relation Analysis — Li 2014"
description: "Large discourse-annotated corpora such as the Penn Discourse Treebank (PDTB) exist for English but not for most other languages."
authors: ["Junyi Jessy Li", "Marine Carpuat", "Ani Nenkova"]
year: 2014
venue: "Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers"
source: "https://aclanthology.org/C14-1055/"
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/li-2014-cross-lingual-discourse-relation-analysis/"
---
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# Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system

**Authors:** [Junyi Jessy Li](https://md.research.elamur.ai/free/authors/junyi-jessy-li/), [Marine Carpuat](https://md.research.elamur.ai/free/authors/marine-carpuat/), [Ani Nenkova](https://md.research.elamur.ai/free/authors/ani-nenkova/) · **Year:** 2014 · **Venue:** Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers · **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/C14-1055/) · [PDF](https://aclanthology.org/C14-1055.pdf) · [OpenAlex](https://openalex.org/W2251385272)

**Citations:** 15 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Large discourse-annotated corpora such as the Penn Discourse Treebank (PDTB) exist for English but not for most other languages. This paper asks whether Chinese explicit discourse relations can be learned without any Chinese discourse annotation by using a parallel corpus instead. The authors drew on the newswire section of a GALE parallel corpus that supplies gold-standard Chinese-English word alignments along with tags. They tagged the English side automatically with a PDTB-trained connective classifier and transferred the labels to the aligned Chinese expressions. A corpus study of this data shows large differences between the two languages in how information is grouped into sentences, in whether relations are marked explicitly or left implicit, and in how ambiguous connectives are. The authors then trained a semi-supervised classifier on the noisy projected labels, using a cascade step to handle unreliable non-discourse labels. On 5-way classification, this system beats a supervised model trained on Chinese Discourse Treebank data. Parallel corpora can therefore substitute for target-language discourse annotation.

## Key points
- **Data:** The GALE newswire portion contains 2,175 articles, 6,255 translation segments and 248,999 Chinese characters. English labels came from the Pitler and Nenkova (2009) classifier, which covers 100 connectives and four PDTB senses plus a non-discourse class. Evaluation used a 170-document CDTB development set, with its eight senses mapped to the PDTB top level.
- **Sentence packaging:** In 1,178 of 6,255 cases (18.83%), one Chinese sentence was translated as several English sentences. These source sentences average about 61 characters, compared with about 36 for 1-1 cases.
- **Explicit/implicit mismatches:** Projection produced a list of 118 Chinese connectives. The share of relations marked explicitly in one language but implicitly in the other ranges from 11.43% for COMPARISON to 30.05% for EXPANSION.
- **5-way classification:** With combined features, the projected semi-supervised system reaches 70.2% accuracy, compared with 63.55% for the supervised CDTB model.
- **Binary per-relation classification:** For three of the four relations, accuracy is only 1.8–3.7% below the supervised system. However, F-measure is lower for every relation except COMPARISON.

*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/) · [Treebanks](https://md.research.elamur.ai/free/topics/treebanks/)

## Cited by

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 10 not yet.

- [GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing](https://md.research.elamur.ai/free/papers/peng-2022-gcdt-chinese-rst-treebank-multigenre/)
- [Fixing Translation Divergences in Parallel Corpora for Neural MT](https://md.research.elamur.ai/free/papers/pham-2018-fixing-translation-divergences-parallel-corpora/)
- [Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/wu-2016-bilingually-constrained-synthetic-data-implicit/)

## Cite

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

```bibtex
@inproceedings{li2014cross,
  title = {{Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system}},
  author = {Li, Junyi Jessy and Carpuat, Marine and Nenkova, Ani},
  booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
  year = {2014},
  url = {https://aclanthology.org/C14-1055/}
}
```

- [← previous Cross-lingual Coreference Resolution of Pronouns](https://md.research.elamur.ai/free/papers/novak-2014-cross-lingual-coreference-resolution-pronouns/)
- [next → Deep Speech: Scaling up end-to-end speech recognition](https://md.research.elamur.ai/free/papers/hannun-2014-deep-speech-scaling-up-end/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/)

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