Modern natural language processing tasks such as text simplification or summarization are typically formulated as monolingual machine translation tasks. This requires appropriate datasets to train, tune, and evaluate the models. This paper describes the creation of a parallel Finnish-Easy Finnish dataset from the Yle News archives. The dataset contains 1919 manually verified pairs of articles, each containing an article in Easy Finnish (selkosuomi) and a corresponding article from Standard Finnish news. Standard Finnish texts total 687555 words, and Easy Finnish texts have 106733 words. This new aligned resource was created automatically based on the Yle News archives from the Language Bank of Finland (Kielipankki) and manually checked by a human expert. The dataset is available for download from Kielipankki. This resource will allow for more effective Easy Language research and for creating applications for automatic simplification and/or summarization of Finnish texts.

Most of the work on Character Networks to date is limited to monolingual texts. Conversely, in this paper we apply and analyze Character Networks on both source texts (English novels) and their Finnish translations (both human- and machine-translated). We assume that this analysis could provide some insights on changes in translations that could modify the character networks, as well as the narrative. The results show that the character networks of translations differ from originals in case of long novels, and the differences may also vary depending on the novel and translator’s strategy.

Character identification is a key element for many narrative-related tasks. To implement it, the baseform of the name of the character (or lemma) needs to be identified, so different appearances of the same character in the narrative could be aligned. In this paper we tackle this problem in translated texts (English–Finnish translation direction), where the challenge regarding lemmatizing foreign names in an agglutinative language appears. To solve this problem, we present and compare several methods. The results show that the method based on a search for the shortest version of the name proves to be the easiest, best performing (83.4% F1), and most resource-independent.