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SEACrowd/id_am2ico

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at 9d3e8b5
Licence files: LICENSE, detected as cc-by-4.0Evidence: LICENSE at 9d3e8b5
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

concept-alignment-classification

Citation text from the card

@inproceedings{liu-etal-2021-am2ico,
    title = "{AM}2i{C}o: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples",
    author = "Liu, Qianchu  and
      Ponti, Edoardo Maria  and
      McCarthy, Diana  and
      Vuli{'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.571",
    doi = "10.18653/v1/2021.emnlp-main.571",
    pages = "7151--7162",
    abstract = "Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics {``}in-context{''} have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, and 3) no support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-cov

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dataset_licence({ "id": "SEACrowd/id_am2ico" })
signal: "unclear"
stated_licence: null
sources: 0