Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 1M<n<10M 10K<n<100K
Licenses: unknown
Language Creators: other
Annotations Creators: other
Source Datasets: original

Dataset Card for Discovery

Dataset Summary

Discourse marker prediction with 174 markers

Supported Tasks and Leaderboards

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Dataset Structure

input : sentence1, sentence2, label: marker originally between sentence1 and sentence2

Data Instances

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Data Fields

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Data Splits


Dataset Creation

Curation Rationale

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Source Data

Aranea english web corpus

Initial Data Collection and Normalization

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Who are the source language producers?

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Self supervised (see paper)

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

    title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
    author = "Sileo, Damien  and
      Van De Cruys, Tim  and
      Pradel, Camille  and
      Muller, Philippe",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "3477--3486",
    abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",


Thanks to @sileod for adding this dataset.

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