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. Furthermore, the corpus is being analyzed regarding its usage and usefulness in other cross-lingual tasks. \\ \newline \Keywords{multilingual embeddings, cross-lingual embeddings, neural machine translation, multi-source translation} }
@InProceedings{HA18.688, author = {Thanh-Le Ha and Jan Niehues and Matthias Sperber and Ngoc Quan Pham and Alexander Waibel}, title = "{KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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