Is Multilingual BERT Fluent in Language Generation?
This work highlights limitations in multilingual BERT for language generation, which is important for NLP practitioners relying on universal models.
The study evaluated multilingual BERT's performance on language generation tasks, finding it inferior to monolingual models, especially for Nordic languages, with English and German models performing well.
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences. We explore how well the model performs on several languages across several tasks: a diagnostic classification probing the embeddings for a particular syntactic property, a cloze task testing the language modelling ability to fill in gaps in a sentence, and a natural language generation task testing for the ability to produce coherent text fitting a given context. We find that the currently available multilingual BERT model is clearly inferior to the monolingual counterparts, and cannot in many cases serve as a substitute for a well-trained monolingual model. We find that the English and German models perform well at generation, whereas the multilingual model is lacking, in particular, for Nordic languages.