  Presented here is a method for automatic punctuation restoration in Swedish
using a BERT model. The method is based on KB-BERT, a publicly available,
neural network language model pre-trained on a Swedish corpus by National
Library of Sweden. This model has then been fine-tuned for this specific task
using a corpus of government texts. With a lower-case and unpunctuated Swedish
text as input, the model is supposed to return a grammatically correct
punctuated copy of the text as output. A successful solution to this problem
brings benefits for an array of NLP domains, such as speech-to-text and
automated text. Only the punctuation marks period, comma and question marks
were considered for the project, due to a lack of data for more rare marks such
as semicolon. Additionally, some marks are somewhat interchangeable with the
more common, such as exclamation points and periods. Thus, the data set had all
exclamation points replaced with periods. The fine-tuned Swedish BERT model,
dubbed prestoBERT, achieved an overall F1-score of 78.9. The proposed model
scored similarly to international counterparts, with Hungarian and Chinese
models obtaining F1-scores of 82.2 and 75.6 respectively. As further
comparison, a human evaluation case study was carried out. The human test group
achieved an overall F1-score of 81.7, but scored substantially worse than
prestoBERT on both period and comma. Inspecting output sentences from the model
and humans show satisfactory results, despite the difference in F1-score. The
disconnect seems to stem from an unnecessary focus on replicating the exact
same punctuation used in the test set, rather than providing any of the number
of correct interpretations. If the loss function could be rewritten to reward
all grammatically correct outputs, rather than only the one original example,
the performance could improve significantly for both prestoBERT and the human
group.
