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Recent advances in the field of language modeling have improved thestate-of-the-art in question answering (QA) and question generation (QG).However, the development of modern neural models, their benchmarks, anddatasets for training them has mainly focused on English. Finnish, like manyother languages, faces a shortage of large QA/QG model training resources,which has prevented experimenting with state-of-the-art QA/QG fine-tuningmethods. We present the first neural QA and QG models that work with Finnish.To train the models, we automatically translate the SQuAD dataset and then usenormalization methods to reduce the amount of problematic data created duringthe translation. Using the synthetic data, together with the Finnish partitionof the TyDi-QA dataset, we fine-tune several transformer-based models to bothQA and QG and evaluate their performance. To the best of our knowledge, theresulting dataset is the first large-scale QA/QG resource for Finnish. Thispaper also sets the initial benchmarks for Finnish-language QA and QG.

Question Answering and Question Generation for Finnish