The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases, intended for training and improving machine translation models. This dataset consists in 4,000 English segments (4,500 tokens) that have been translated into each of 26 low-resource languages, as well as three higher-resource pivot languages (es, fr, hi). All translations were made directly from English, with the exception of Aymara, which was translated from the Spanish.
Dataset Description The dataset used in this study comprises bug reports extracted from the Visual Studio Code GitHub repository, specifically focusing on those labeled with the english-please tag. This label indicates that the original submission was written in a language other than English, providing a clear signal for multilingual content. The dataset spans a five-year period (March 2019--June 2024), ensuring a diverse representation of bug types, user environments, and technical contexts. ### Characteristics The dataset contains 1,381 multilingual bug reports, each consisting of: - The original bug report written in a non-English language. - A translated version in English. - Metadata such as issue number, creation date, labels, and status. - Categorization into functional, UI, and performance-related issues based on the content. ### Motivation & Summary This dataset is motivated by the need to improve multilingual bug tracking and translation evaluation. Given the increasing globalization of software development, developers and QA teams frequently encounter bug reports in languages they do not understand. By providing a structured corpus of translated bug reports, this dataset facilitates: - Comparative translation evaluation (e.g., ChatGPT vs AWS Translate vs DeepL). - Linguistic analysis of technical bug reporting across different languages. - Insights into common software issues encountered by diverse users. - Improving multilingual issue tracking through automated labeling and categorization. ### Potential Use Cases This dataset can be beneficial for various research and development applications, including: - Machine Translation Benchmarking: Evaluating the performance of translation models in a technical domain. - Natural Language Processing (NLP) Tasks: Training classifiers to categorize bug reports based on their content. - Software Engineering Research: Understanding trends in bug reporting, issue resolution, and localization challenges. - Automated Bug Triage: Developing AI-driven solutions for assigning and prioritizing bug reports in multilingual repositories.