metadata

annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
pretty_name: Extended Paraphrase Typology Corpus

Dataset Card for [Dataset Name]

Dataset Card for [Dataset Name]

Dataset Description

Homepage: https://github.com/venelink/ETPC/
Repository:
Paper: ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation
Leaderboard:
Point of Contact:

Dataset Summary

We present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification.

Supported Tasks and Leaderboards

text-classification

Languages

The text in the dataset is in English (en).

Dataset Structure

Data Fields

idx: Monotonically increasing index ID.
sentence1: Complete sentence expressing an opinion about a film.
sentence2: Complete sentence expressing an opinion about a film.
etpc_label: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to etpc annotation schema.
mrpc_label: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to mrpc annotation schema.
negation: Whether on sentence is a negation of another, either "yes" (1) or "no" (0).

Data Splits

train: 5801

Citation Information

If you use the dataset in any way, please cite the following paper. Preprint: https://arxiv.org/abs/2310.14863

@inproceedings{kovatchev-etal-2018-etpc,
    title = "{ETPC} - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation",
    author = "Kovatchev, Venelin  and
      Mart{\'\i}, M. Ant{\`o}nia  and
      Salam{\'o}, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1221",
}

@inproceedings{wahle-etal-2023-paraphrase,
    title = "Paraphrase Types for Generation and Detection",
    author = "Wahle, Jan Philip  and
      Gipp, Bela  and
      Ruas, Terry",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.746/",
    doi = "10.18653/v1/2023.emnlp-main.746",
    pages = "12148--12164",
    abstract = "Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this shortcoming by considering paraphrase types - specific linguistic perturbations at particular text positions. We name these tasks Paraphrase Type Generation and Paraphrase Type Detection. Our results suggest that while current techniques perform well in a binary classification scenario, i.e., paraphrased or not, the inclusion of fine-grained paraphrase types poses a significant challenge. While most approaches are good at generating and detecting general semantic similar content, they fail to understand the intrinsic linguistic variables they manipulate. Models trained in generating and identifying paraphrase types also show improvements in tasks without them. In addition, scaling these models further improves their ability to understand paraphrase types. We believe paraphrase types can unlock a new paradigm for developing paraphrase models and solving tasks in the future."
}

Contributions

Thanks to @jpwahle for adding this dataset.

Datasets:

jpwahle
/

etpc