Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
This addresses the lack of evaluation resources for low-resourced languages, offering datasets and benchmarks for Yorùbá and Twi, though it is incremental as it applies existing methods to new languages.
The paper tackled the problem of evaluating word embeddings for low-resourced languages like Yorùbá and Twi by comparing embeddings from massive unannotated text versus curated corpora, finding that quality improvements depend on data quality, not just quantity, with manual translation of test sets provided.
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.