DGurgurov/danish_sa
收藏数据集概述
数据集名称
Sentiment Analysis Data for the Danish Language
数据集描述
本数据集包含由Isbister等人于2021年发布的情感分析数据。
数据结构
该数据用于改进低资源语言的图知识词嵌入项目。
语言
丹麦语（da）
任务类别
文本分类
许可证
MIT
引用信息
bibtex @inproceedings{isbister-etal-2021-stop, title = "Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?", author = "Isbister, Tim and Carlsson, Fredrik and Sahlgren, Magnus", editor = "Dobnik, Simon and {O}vrelid, Lilja", booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may # " 31--2 " # jun, year = "2021", address = "Reykjavik, Iceland (Online)", publisher = {Link{"o}ping University Electronic Press, Sweden}, url = "https://aclanthology.org/2021.nodalida-main.42", pages = "385--390", abstract = "Most work in NLP makes the assumption that it is desirable to develop solutions in the native language in question. There is consequently a strong trend towards building native language models even for low-resource languages. This paper questions this development, and explores the idea of simply translating the data into English, thereby enabling the use of pretrained, and large-scale, English language models. We demonstrate empirically that a large English language model coupled with modern machine translation outperforms native language models in most Scandinavian languages. The exception to this is Finnish, which we assume is due to inferior translation quality. Our results suggest that machine translation is a mature technology, which raises a serious counter-argument for training native language models for low-resource languages. This paper therefore strives to make a provocative but important point. As English language models are improving at an unprecedented pace, which in turn improves machine translation, it is from an empirical and environmental stand-point more effective to translate data from low-resource languages into English, than to build language models for such languages.", }
