  Big languages such as English and Finnish have many natural language
processing (NLP) resources and models, but this is not the case for
low-resourced and endangered languages as such resources are so scarce despite
the great advantages they would provide for the language communities. The most
common types of resources available for low-resourced and endangered languages
are translation dictionaries and universal dependencies. In this paper, we
present a method for constructing word embeddings for endangered languages
using existing word embeddings of different resource-rich languages and the
translation dictionaries of resource-poor languages. Thereafter, the embeddings
are fine-tuned using the sentences in the universal dependencies and aligned to
match the semantic spaces of the big languages; resulting in cross-lingual
embeddings. The endangered languages we work with here are Erzya, Moksha,
Komi-Zyrian and Skolt Sami. Furthermore, we build a universal sentiment
analysis model for all the languages that are part of this study, whether
endangered or not, by utilizing cross-lingual word embeddings. The evaluation
conducted shows that our word embeddings for endangered languages are
well-aligned with the resource-rich languages, and they are suitable for
training task-specific models as demonstrated by our sentiment analysis model
which achieved a high accuracy. All our cross-lingual word embeddings and the
sentiment analysis model have been released openly via an easy-to-use Python
library.
