From web to dialects: how to enhance non-standard Russian lects lemmatisation?

P. 167–175.

Afanasev I., Lyashevskaya O.

The growing need for using small data distinguished by a set of distributional properties becomes all the more apparent in the era of large language models (LLM). In this paper, we show that for the lemmatisation of the web as corpora texts, heterogeneous social media texts, and dialect texts, the morphological tagging by a model trained on a small dataset with specific properties generally works better than the morphological tagging by a model trained on a large dataset. The material we use is Russian non-standard texts and interviews with dialect speakers. The sequence-to-sequence lemmatisation with the help of taggers trained on smaller linguistically aware datasets achieves the average results of 85 to 90 per cent. These results are consistently (but not always), by 1-2 per cent. higher than the results of lemmatisation with the help of the large-dataset-trained taggers. We analyse these results and outline the possible further research directions.

Keywords: Russian language, morphological tagging, lemmatization, Russian dialects, learning with small data

In book

Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)

Gothenburg: Association for Computational Linguistics, 2023.