---
title: "Mining Discourse Markers for Unsupervised — Sileo 2019"
description: "Supervised sentence encoders depend on costly annotated corpora such as SNLI."
authors: ["Damien Sileo", "Tim Van De Cruys", "Camille Pradel", "Philippe Muller"]
year: 2019
venue: "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)"
source: "https://aclanthology.org/N19-1351/"
doi: "10.18653/v1/n19-1351"
arxiv: "1903.11850"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/sileo-2019-mining-discourse-markers-unsupervised-sentence/"
---
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# Mining Discourse Markers for Unsupervised Sentence Representation Learning

**Authors:** [Damien Sileo](https://md.research.elamur.ai/free/authors/damien-sileo/), [Tim Van De Cruys](https://md.research.elamur.ai/free/authors/tim-van-de-cruys/), [Camille Pradel](https://md.research.elamur.ai/free/authors/camille-pradel/), [Philippe Muller](https://md.research.elamur.ai/free/authors/philippe-muller/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N19-1351/) · [PDF](https://aclanthology.org/N19-1351.pdf) · [DOI 10.18653/v1/n19-1351](https://doi.org/10.18653/v1/n19-1351) · [arXiv 1903.11850](https://arxiv.org/abs/1903.11850) · [OpenAlex](https://openalex.org/W2924120895)

**Citations:** 34 citations (openalex-snapshot · 2026-10-14) · [49](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Supervised sentence encoders depend on costly annotated corpora such as SNLI. Earlier attempts to use discourse markers as free supervision covered only a few markers or coarse categories and drew on the small BookCorpus. This paper presents a way to mine sentence pairs linked by explicit markers from web-scale text. It uses a Rutherford and Xue-style rule: the second sentence begins with a single-word adverbial or conjunction followed by a comma. The rule is not limited to a fixed inventory, so it surfaces new markers, and PDTB markers are added on top. Applied to the Depcc corpus (Common Crawl), the method produces the Discovery dataset: 174 English markers, each with at least 10K examples, including rare ones like "coincidentally." Encoders are trained in the InferSent framework to predict the marker for a sentence pair. On SentEval transfer and probing tasks they match or beat strong baselines on most tasks. Analysis shows that many markers are predictable from shallow lexical cues alone. Shuffling sentence pairs barely hurts the models, which suggests they do not truly exploit inter-sentence relations.

## Key points
- From 8.5 billion consecutive sentence pairs sampled from Depcc, filtering kept 53%. About 3.77% of those (≈170M pairs) contained a candidate marker. After keeping markers with ≥10K occurrences and capping each at 200K, the dataset has 19M pairs.
- 243 candidate markers were found, giving a curated set of 174. The rule recovered 31 of the 42 single-word PDTB markers that precede a comma.
- A Fasttext classifier was used to measure how predictable each marker is from shallow features. Removing these "easy" examples (DiscoveryHard) did not improve transfer results.
- On the SentEval linguistic probing tasks, DiscoveryBig averages 75.5. This compares with 70.8 for InferSent, 72.7 for SkipThought and 71.4 for QuickThought.
- The dataset and code are released at github.com/synapse-developpement/Discovery. The authors argue that the method could be adapted to other languages with similar discourse usage, such as French or German.

*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

49 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 36 not yet.

- [Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/papers/chan-2024-exploring-potential-chatgpt-sentence-level/)
- [Comparing PTB and UD information for PDTB discourse connective identification](https://md.research.elamur.ai/free/papers/han-2020-comparing-ptb-ud-information-pdtb/)
- [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://md.research.elamur.ai/free/papers/jiang-2022-blonde-automatic-evaluation-metric-document/)
- [Top-down Discourse Parsing via Sequence Labelling](https://md.research.elamur.ai/free/papers/koto-2021-top-down-discourse-parsing-sequence/)
- [Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations](https://md.research.elamur.ai/free/papers/lee-2020-weakly-supervised-modeling-contextualized-event/)
- [Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision](https://md.research.elamur.ai/free/papers/li-2024-discourse-relation-prediction-discourse-parsing/)
- [Long-context LLMs Struggle with Long In-context Learning](https://md.research.elamur.ai/free/papers/li-2024-long-context-llms-struggle-long/)
- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://md.research.elamur.ai/free/papers/liu-2023-annotation-inspired-implicit-discourse-relation/)
- [Pragmatic competence of pre-trained language models through the lens of discourse connectives](https://md.research.elamur.ai/free/papers/pandia-2021-pragmatic-competence-pre-trained-language/)
- [A Pragmatics-Centered Evaluation Framework for Natural Language Understanding](https://md.research.elamur.ai/free/papers/sileo-2022-pragmatics-centered-evaluation-framework-natural/)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://md.research.elamur.ai/free/papers/sun-2019-ernie-2-0-continual/)
- [Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric](https://md.research.elamur.ai/free/papers/vernikos-2022-embarrassingly-easy-document-level-mt/)

## Cite

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

```bibtex
@inproceedings{sileo2019mining,
  title = {{Mining Discourse Markers for Unsupervised Sentence Representation Learning}},
  author = {Sileo, Damien and Cruys, Tim Van De and Pradel, Camille and Muller, Philippe},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1351},
  eprint = {1903.11850},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n19-1351}
}
```

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