Serhii Hamotskyi


2024

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Development and Evaluation of a German Language Model for the Financial Domain
Nata Kozaeva | Serhii Hamotskyi | Christian Hanig
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing @ LREC-COLING 2024

Recent advancements in self-supervised pre-training of Language Models (LMs) have significantly improved their performance across a wide range of Natural Language Processing (NLP) tasks. Yet, the adaptation of these models to specialized domains remains a critical endeavor, as it enables the models to grasp domain-specific nuances, terminology, and patterns more effectively, thereby enhancing their utility in specialized contexts. This paper presents an in-depth investigation into the training and fine-tuning of German language models specifically for the financial sector. We construct various datasets for training and fine-tuning to examine the impact of different data construction strategies on the models’ performance. Our study provides detailed insights into essential pre-processing steps, including text extraction from PDF documents and language identification, to evaluate their influence on the performance of the language models. Addressing the scarcity of resources in the German financial domain, we also introduce a German Text Classification benchmark dataset, aimed at fostering further research and development in this area. The performance of the trained models is evaluated on two domain-specific tasks, demonstrating that fine-tuning with domain-specific data improves model outcomes, even with limited amounts of domain-specific data.

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FinCorpus-DE10k: A Corpus for the German Financial Domain
Serhii Hamotskyi | Nata Kozaeva | Christian Hänig
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We introduce a predominantly German corpus comprising 12.5k PDF documents sourced from the financial domain. The corresponding extracted textual data encompasses more than 165 million tokens derived predominantly from German, and to a lesser extent, bilingual documents. We provide detailed information about the document types included in the corpus, such as final terms, base prospectuses, annual reports, information materials, law documents, international financial reporting standards, and monthly reports from the Bundesbank, accompanied by comprehensive statistical analysis. To our knowledge, it is the first non-email German financial corpus available, and we hope it will fill this gap and foster further research in the financial domain both in the German language and in multilingual contexts.

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Eval-UA-tion 1.0: Benchmark for Evaluating Ukrainian (Large) Language Models
Serhii Hamotskyi | Anna-Izabella Levbarg | Christian Hänig
Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024

In this paper, we introduce Eval-UA-tion, a set of novel Ukrainian-language datasets aimed at evaluating the performance of language models on the Ukrainian language. The tasks include UA-CBT (inspired by the Children’s Book Test, a fill-in-the-gaps type task aimed at gauging the extent to which a story narrative is understood), UP-Titles (where the online newspaper Ukrainska Pravda‘s articles have to be matched to the correct title among 10 similar ones), and LMentry-static-UA/LMES (inspired by the LMentry benchmark, a set of tasks simple to solve for humans but hard for LMs, such as ‘which of these words is longer’ and ‘what is the fifth word of this sentence’). With the exception of UP-Titles, the tasks are built in a way to minimize contamination and use material unlikely to be present in the training sets of language models, and include a split for few-shot model prompting use that minimizes contamination. For each task human and random baselines are provided.