This paper presents Murreviikko, a dataset of dialectal Finnish tweets which have been dialectologically annotated and manually normalized to a standard form. The dataset can be used as a test set for dialect identification and dialect-to-standard normalization, for instance. We evaluate the dataset on the normalization task, comparing an existing normalization model built on a spoken dialect corpus and three newly trained models with different architectures. We find that there are significant differences in normalization difficulty between the dialects, and that a character-level statistical machine translation model performs best on the Murreviikko tweet dataset.

Language label tokens are often used in multilingual neural language modeling and sequence-to-sequence learning to enhance the performance of such models. An additional product of the technique is that the models learn representations of the language tokens, which in turn reflect the relationships between the languages. In this paper, we study the learned representations of dialects produced by neural dialect-to-standard normalization models. We use two large datasets of typologically different languages, namely Finnish and Norwegian, and evaluate the learned representations against traditional dialect divisions of both languages. We find that the inferred dialect embeddings correlate well with the traditional dialects. The methodology could be further used in noisier settings to find new insights into language variation.