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mteb/amazon_counterfactual

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Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at b9867e0
Licence files: No licence file at the top level of the repositoryEvidence: file list at b9867e0
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

text-classification

Citation text from the card

@inproceedings{oneill-etal-2021-wish,
  abstract = {Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.},
  address = {Online and Punta Cana, Dominican Republic},
  author = {O{'}Neill, James  and
Rozenshtein, Polina  and
Kiryo, Ryuichi  and
Kubota, Motoko  and
Bollegala, Danushka},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  doi = {10.18653/v1/2021.emnlp-main.568},
  editor = {Moens, Marie-Francine  and
Huang, Xuanjing  and
Specia, Lucia  and
Yih, Scott Wen-tau},
  mon

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dataset_licence({ "id": "mteb/amazon_counterfactual" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0