大规模与低资源语言的精选词嵌入。Yorkebá和Twi案
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
摘要：
几种架构从未注释的文本中学习语义表示的成功，以及这些文本在在线多语言资源（如维基百科）中的可用性，促进了多语言资源的大规模自动创建。对这些资源的评估通常是针对高资源语言进行的，在这些语言中，人们有大量的任务和测试集需要评估。对于低资源语言，评估更加困难，通常被忽略，希望深度学习架构在高资源环境中学习（多语言）表示的令人印象深刻的能力也适用于低资源环境。在本文中，我们重点关注两种非洲语言，Yorūbá和Twi，并将以这种方式获得的词嵌入与从精选语料库和语言相关处理中获得的词嵌入进行比较。我们分析了公开语料库中的噪音，收集了两种语言的高质量和噪音数据，并量化了不仅取决于数据量而且取决于质量的改进。我们还使用不同的架构，从表面形式和字符中学习单词表示，以进一步利用所有可用的信息，这些信息对这些语言很重要。为了进行评估，我们将wordsim-353单词对数据集从英语手动翻译为Yorkbá和Twi。作为输出的工作，我们提供语料库，嵌入和测试适合这两种语言。
英文摘要：
The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yorùbá and Twi. As output of the work, we provide corpora, embeddings and the test suits for both languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
