The paper describes problems in disambiguating the morphological analysis of Bantu languages by using Swahili as a test language. The main factors of ambiguity in this language group can be traced to the noun class structure on one hand and to the bi-directional word-formation on the other. In analyzing word-forms, the system applied utilizes SWATWOL, a morphological parsing program based on two-level formalism. Disambiguation is carried out with the latest version (April 1996) of the Constraint Grammar Parser (GGP). Statistics on ambiguity are provided. Solutions tbr resolving different types of ambiguity are presented and they are demonstrated by examples fi'om corpus text. Finally, statistics on the performance of the disambiguator are presented.

The paper describes a rule-based machine translation system adapted to English to Finnish translation. Although the translation system participates in the shared task of news translation in WMT 2017, the paper describes the strengths and weaknesses of the approach in general.

This paper describes the University of Helsinki's submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions. This year, our main submissions employ a novel neural architecture, the Transformer, using the open-source OpenNMT framework. Our experiments couple domain labeling and fine tuned multilingual models with shared vocabularies between the source and target language, using the provided parallel data of the shared task and additional back-translations. Finally, we compare, for the English-to-Finnish case, the effectiveness of different machine translation architectures, starting from a rule-based approach to our best neural model, analyzing the output and highlighting future research.

In this paper, we present the University of Helsinki submissions to the WMT 2019 shared task on news translation in three language pairs: English-German, English-Finnish and Finnish-English. This year, we focused first on cleaning and filtering the training data using multiple data-filtering approaches, resulting in much smaller and cleaner training sets. For English-German, we trained both sentence-level transformer models and compared different document-level translation approaches. For Finnish-English and English-Finnish we focused on different segmentation approaches, and we also included a rule-based system for English-Finnish.

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