Question Answering and Question Generation for Finnish
This work addresses the resource gap for Finnish in NLP, enabling state-of-the-art QA and QG applications, but it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of large-scale question answering and question generation resources for Finnish by creating the first neural models for this language, achieving initial benchmarks through fine-tuning transformer-based models on synthetic and existing datasets.
Recent advances in the field of language modeling have improved the state-of-the-art in question answering (QA) and question generation (QG). However, the development of modern neural models, their benchmarks, and datasets for training them has mainly focused on English. Finnish, like many other languages, faces a shortage of large QA/QG model training resources, which has prevented experimenting with state-of-the-art QA/QG fine-tuning methods. 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 use normalization methods to reduce the amount of problematic data created during the translation. Using the synthetic data, together with the Finnish partition of the TyDi-QA dataset, we fine-tune several transformer-based models to both QA and QG and evaluate their performance. To the best of our knowledge, the resulting dataset is the first large-scale QA/QG resource for Finnish. This paper also sets the initial benchmarks for Finnish-language QA and QG.