High-quality datasets are crucial for training effective state of the art machine translation systems. However, due to the data-intensive nature of these systems, they have to be trained on large amounts of text that can easily go beyond the scope of full human inspection. This makes the presence of noise that can degrade overall system performance a frequent and significant issue. While various approaches have been developed to identify and select only the highest-quality training examples, this is undesirable in scenarios where resources are limited. For this reason, we introduce AlignFix, an open-source tool for augmenting data, identifying and correcting errors in parallel corpora. Leveraging word alignments, AlignFix extracts consistent phrase pairs, enabling targeted replacements that can improve the dataset quality. Besides targeted replacements, the tool enables contextual augmentation by duplicating sentences and allowing users to substitute words with alternatives of their choice. The tool maintains and updates the underlying word alignments, thereby avoiding the costly recomputation. AlignFix runs locally in the browser, requires no installation, and ensures that all data remains entirely on the client side. It is released under Apache 2.0 license, encouraging broad adoption, reuse, and further development. A live demo is available at https://ifi-alignfix.uibk.ac.at.

While machine translation has taken great strides in recent years, thanks in large part to transformer language models, machine translation tools are designed primarily for plain text, and thus not equipped to deal with complex markup documents. Not even Large Language Models can reliably handle LaTeX source files, as non-standard structures are not captured by any available training data. Previous attempts to create translation engines for LaTeX either work on compiled documents, rely on document pre-processors which may lose critical semantic elements, or cannot distinguish between text and non-text content. In this paper we present LaTeXMT, a software solution for structure-preserving, source-to-source translation of LaTeX documents. All of the source code to LaTeXMT is provided under the LGPL-3.0 open-source licence and a web version is publicly available.

Recent advances in neural machine translation (NMT) have opened new possibilities for developing translation systems also for smaller, so-called low-resource, languages. The rise of large language models (LLMs) has further revolutionized machine translation by enabling more flexible and context-aware generation. However, many challenges remain for low-resource languages, and the availability of high-quality, validated test data is essential to support meaningful development, evaluation, and comparison of translation systems. In this work, we present an extension of the FLORES+ dataset for two Ladin variants, Val Badia and Gherdëina, as a submission to the Open Language Data Initiative Shared Task 2025. To complement existing resources, we additionally release two parallel datasets for Gherdëina–Val Badia and Gherdëina–Italian. We validate these datasets by evaluating state-of-the-art LLMs and NMT systems on this test data, both with and without leveraging the newly released parallel data for fine-tuning and prompting. The results highlight the considerable potential for improving translation quality in Ladin, while also underscoring the need for further research and resource development, for which this contribution provides a basis.

This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.