{
 "id": "HiTZ/Multilingual-Opinion-Target-Extraction",
 "owner": "HiTZ",
 "name": "Multilingual-Opinion-Target-Extraction",
 "downloads": 84,
 "likes": 2,
 "last_modified": "2023-11-22T13:32:07.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "token-classification"
 ],
 "citation": "@inproceedings{garcia-ferrero-etal-2022-model,\n    title = \"Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings\",\n    author = \"Garc{\\'\\i}a-Ferrero, Iker  and\n      Agerri, Rodrigo  and\n      Rigau, German\",\n    editor = \"Goldberg, Yoav  and\n      Kozareva, Zornitsa  and\n      Zhang, Yue\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2022\",\n    month = dec,\n    year = \"2022\",\n    address = \"Abu Dhabi, United Arab Emirates\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2022.findings-emnlp.478\",\n    doi = \"10.18653/v1/2022.findings-emnlp.478\",\n    pages = \"6403--6416\",\n    abstract = \"Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to impor",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "98355e3b92afb9f92072cbd91dba766f68a70d9a",
 "stated_evidence": "https://huggingface.co/datasets/HiTZ/Multilingual-Opinion-Target-Extraction/blob/98355e3b92afb9f92072cbd91dba766f68a70d9a/README.md",
 "file_evidence": "https://huggingface.co/datasets/HiTZ/Multilingual-Opinion-Target-Extraction/tree/98355e3b92afb9f92072cbd91dba766f68a70d9a",
 "card_nc": false
}