This article studies register classification of documents from the unrestricted web, such as news articles or opinion blogs, in a multilingual setting, exploring both the benefit of training on multiple languages and the capabilities for zero-shot cross-lingual transfer. While the wide range of linguistic variation found on the web poses challenges for register classification, recent studies have shown that good levels of cross-lingual transfer from the extensive English CORE corpus to other languages can be achieved. In this study, we show that training on multiple languages 1) benefits languages with limited amounts of register-annotated data, 2) on average achieves performance on par with monolingual models, and 3) greatly improves upon previous zero-shot results in Finnish, French and Swedish. The best results are achieved with the multilingual XLM-R model. As data, we use the CORE corpus series featuring register annotated data from the unrestricted web.

We explore cross-lingual transfer of register classification for web documents. Registers, that is, text varieties such as blogs or news are one of the primary predictors of linguistic variation and thus affect the automatic processing of language. We introduce two new register-annotated corpora, FreCORE and SweCORE, for French and Swedish. We demonstrate that deep pre-trained language models perform strongly in these languages and outperform previous state-of-the-art in English and Finnish. Specifically, we show 1) that zero-shot cross-lingual transfer from the large English CORE corpus can match or surpass previously published monolingual models, and 2) that lightweight monolingual classification requiring very little training data can reach or surpass our zero-shot performance. We further analyse classification results finding that certain registers continue to pose challenges in particular for cross-lingual transfer.

We present a new manually annotated corpus for broad-coverage named entity recognition for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 documents (200,000 tokens) representing ten different genres of text, we introduce annotation marking person, organization, location, product and event names as well as dates. The new annotation identifies in total over 10,000 mentions. An evaluation of inter-annotator agreement indicates that the quality and consistency of annotation are high, at 94.5% F-score for exact match. A comprehensive evaluation using state-of-the-art machine learning methods demonstrates that the new resource maintains compatibility with a previously released single-domain corpus for Finnish NER and makes it possible to recognize named entity mentions in texts drawn from most domains at precision and recall approaching or exceeding 90%. Remaining challenges such as the identification of names in blog posts and transcribed speech are also identified. The newly introduced Turku NER corpus and related resources introduced in this work are released under open licenses via https://turkunlp.org/turku-ner-corpus .