Kit-Multi: A translation-oriented multilingual embedding corpus

Cross-lingual word embeddings are the representations of words across languages in a shared continuous vector space. Cross-lingual
word embeddings have been shown to be helpful in the development of cross-lingual natural language processing tools. In case of more
than two languages involved, we call them multilingual word embeddings. In this work, we introduce a multilingual word embedding
corpus which is acquired by using neural machine translation. Unlike other cross-lingual embedding corpora, the embeddings can be
learned from significantly smaller portions of data and for multiple languages at once. An intrinsic evaluation on monolingual tasks
shows that our method is fairly competitive to the prevalent methods but on the cross-lingual document classification task, it obtains the
best figures. We are in the process to produce the embeddings for more languages, especially the languages which belong to the same
family or sematically close to each others, such as Japanese-Korean, Chinese-Vietnamese, German-Dutch, or Latin-based languagues.
Furthermore, the corpus is being analyzedd regarding its usage and usefulness in other cross-lingual tasks.