社交媒体和NLP任务:语言信息众包中的挑战

Eirini Takoulidou, Vilelmini Sosoni, Katia Lida Kermanidis, Menno van Zaanen
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引用次数: 3

摘要

在TraMOOC1(Massive Open Online Courses Translation for Massive Open Online Courses)研究创新项目的框架下,平行翻译的数据收集任务使用众包平台实现。源数据的教育类型(视频讲座字幕、论坛讨论、课程作业)、文本类型(切分、拼写错误、语法错误、专业术语、科学公式、有限的上下文知识)以及所涉及活动的多语言方法(重点是12种欧洲和金砖四国语言)为项目的成功提供了一个具有挑战性的环境。实验试验揭示了语言技术研究的重要发现,以及多语言任务众包语言数据收集的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social media and NLP tasks: Challenges in crowdsourcing linguistic information
In the framework of the TraMOOC1(Translation for Massive Open Online Courses) research and innovation project, data collection tasks for parallel translation are implemented using a crowdsourcing platform. The educational genre (videolectures subtitles, forums discussions, course assignments), the type of text (segmentation, misspellings, syntax errors, specialized terminology, scientific formulas, limited knowledge on context) of the source data, and the multilingual approach of the involved activities (the focus is on a total of 12 European and BRIC languages) provides a challenging setting for the success of the project. Experimental trials reveal significant findings for the purposes of Language Technology research as well as limitations in crowdsourcing linguistic data collections for multilingual tasks.
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