Keep me PoS-ted: experimenting with Part-of-Speech prediction on Old Babylonian letters

Abstract

Within this paper we will account for a cooperation between Ghent University based Assyriologists and computational linguists that has set up a pilot study to analyse the language used in Old Babylonian (OB) letters using Natural Language Processing (NLP) techniques. OB letters make up an interesting dataset because (1) they form an invaluable source for everyday vernacular language, and (2) more than 5000 have been recovered, many of which are accessible in transliteration and translation through the series Altbabylonische Briefe and the Cuneiform Digital Library Initiative. Based on a first batch of letters from OB Sippar, later extended by other Akkadian letters, we aim to develop machine learning approaches to perform semi-automatic text analysis and annotation of the letters. We will here present a Part-of-Speech (PoS) tag prediction model using machine learning. The input data is Akkadian in transliteration and the best performing model is a fine-tuned Multilingual BERT Transformer with Word embeddings (weighted avg F1: 90.19 %). When compared to the benchmark attempt of PoS tagging on a larger Akkadian corpus (97.67 %), it leaves room for improvement. However, analysing the results shows us that multilingual word embeddings improve the model performance and with an enlargement of the corpus targeting certain classes, we could considerably better the macro average F1 scores.