This study examines the effectiveness of adaptive machine translation (AMT) for gender-neutral language (GNL) use in English-German translation using the ModernMT engine. It investigates gender bias in initial output and adaptability to two distinct GNL strategies, as well as the influence of translation memory (TM) use on adaptivity. Findings indicate that despite inherent gender bias, machine translation (MT) systems show potential for adapting to GNL with appropriate exposure and training, highlighting the importance of customisation, exposure to diverse examples, and better representation of different forms for enhancing gender-fair translation strategies.

This paper describes the process of creating a Bulgarian-English parallel corpus for the purposes of constructing a statistical translation model for verb forms in both languages. We briefly introduce the scientific problem behind the corpus, its main purpose, general conception, linguistic resources and annotation conception. In more details we describe the collection of language data for the purposes of creating the corpus, the preparatory processing of the gathered data, the annotation rules based on the characteristics of the gathered data and the chosen software. We discuss the current work on the training model and the future work on this linguistic resource and the aims of the scientific project.

The paper‘s main subject is concerned with the problems related to machine translation of verb forms from Bulgarian to English. In separate sections of this article we discuss the problems related to differences between word formation in both languages and differences in the information that the verb forms grammaticalize. We also introduce the idea of implementing the statistical method of machine translation altogether with the rule-based method as a proposal for future research and the possible practical and theoretical outcomes.