{
 "id": "SEACrowd/id_am2ico",
 "owner": "SEACrowd",
 "name": "id_am2ico",
 "downloads": 127,
 "likes": 0,
 "last_modified": "2024-06-24T13:25:53.000Z",
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 "file_licence": "cc-by-4.0",
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 "tasks": [
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 ],
 "citation": "@inproceedings{liu-etal-2021-am2ico,\n    title = \"{AM}2i{C}o: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples\",\n    author = \"Liu, Qianchu  and\n      Ponti, Edoardo Maria  and\n      McCarthy, Diana  and\n      Vuli{'c}, Ivan  and\n      Korhonen, Anna\",\n    booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2021\",\n    address = \"Online and Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.emnlp-main.571\",\n    doi = \"10.18653/v1/2021.emnlp-main.571\",\n    pages = \"7151--7162\",\n    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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