Question Answering and Question Generation for Finnish

Ilmari Kylliäinen, Roman Yangarber

Keywords: computational linguistics, question answering, question generation, deep learning, transformer models

TL;DR: We introduce the first monolingual model for Question Answering and Question Generation in Finnish. We also introduce a new evaluation corpus, which is released to the research community

Abstract: 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.

Student Paper: Yes, the first author is a student

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:2211.13794/code)