The performance of multilingual pretrained models is highly dependent on the
availability of monolingual or parallel text present in a target language.
Thus, the majority of the world's languages cannot benefit from recent progress
in NLP as they have no or limited textual data. To expand possibilities of
using NLP technology in these under-represented languages, we systematically
study strategies that relax the reliance on conventional language resources
through the use of bilingual lexicons, an alternative resource with much better
language coverage. We analyze different strategies to synthesize textual or
labeled data using lexicons, and how this data can be combined with monolingual
or parallel text when available. For 19 under-represented languages across 3
tasks, our methods lead to consistent improvements of up to 5 and 15 points
with and without extra monolingual text respectively. Overall, our study
highlights how NLP methods can be adapted to thousands more languages that are
under-served by current technology

本文探究了利用双语词典作为文本资源，从而使得多语言预训练模型的性能不再依赖于单一语种下的文本数据，通过不同策略合成文本或标注数据，并与单语文本或平行文本相结合，以提高在三个任务上 19 种欠发达语言的性能，为千余种使用 NLP 技术的欠代表语种提供了思路。