Even though Machine Translation can reduce your workload by translating expressions to other languages, it comes at a great cost in performance. Depending on the language, you lose between 15 and 70%.To compensate for this gap, a lot of manual work will be required to enrich the other languages with additional expressions.
With Chatlayer’s Language Independent NLP there is nearly no performance difference going from one language to another. This means you can launch a bot in any number of languages. If you trained your model in only one language, you only need to enriched it with some very language specific expressions.
[1] Conneau, Alexis, et al. “XNLI: Evaluating Cross-lingual Sentence Representations.” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
[2] Conneau, Alexis, and Guillaume Lample. “Cross-lingual Language Model Pretraining.” Advances in Neural Information Processing Systems. 2019.
[3] Chi, Zewen, et al. “Cross-Lingual Natural Language Generation via Pre-Training.” arXiv preprint arXiv:1909.10481 (2019).