An Analysis of Language Frequency and Error Correction for Esperanto

Current Grammar Error Correction (GEC) systems predominantly target major languages like English[1, 2, 3], Chinese[4, 5], German[6] and Japanese[7]. This focus is driven by the availability of comprehensive datasets and the specific linguistic characteristics inherent to these languages. Consequently, the exploration of GEC methodologies for low-resource languages has been largely overlooked, leaving a significant gap in the analysis and development of error correction strategies for these less-studied languages. Recently, Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by equipping these models with the ability to generate text that close to human language. LLMs have attracted considerable attention for their proficiency in English language tasks. Recent studies, however, reveal their potential across various languages. Despite this broad applicability, our analysis identifies a notable gap in the research landscape, particularly concerning Esperanto. As a constructed language, Esperanto presents unique challenges in terms of frequency distribution and grammar error correction that have yet to be thoroughly explored. This article delves into the word and letter frequency specific to Esperanto and embarks on a preliminary investigation into the capabilities of GPT-3.5 and GPT-4--innovations by OpenAI