The Anglocentric nature of scholarly communication has many implications, such as limiting publication, discoverability and access from other language communities (even for major languages); putting minoritized languages at risk in the academic domain; and excluding many from peer review. The OSCAIL project addresses these challenges by exploring how machine translation (MT) enhanced by large language model (LLM)–based technologies can support access to scientific knowledge. Outputs will include evaluation datasets, protocols and best practices for MT in scholarly communication, and a prototype integration of MT tools into Open Journal Systems, the world’s most widely used open-source scholarly publishing platform.

We present two open-weight translation models for Estonian and its low-resource "relatives" in the Finno-Ugric language family. The training data includes 12 languages paired with Estonian as well as 23 more Finno-Ugric languages and varieties, ranging from mid-resource examples with tens of thousands of speakers to extremely low-resource critically endangered languages with less than a hundred speakers. The translation models use Unbabel Tower+ 2B and 9B as their starting point. We compare their performance on two benchmarks to DeepL and GPT-5.2 and show that in most cases we surpass the quality of DeepL and match or nearly match the quality of GPT-5.2’s output with just a fraction of the parameters. Among other contributions we also restore the paragraph structure of a massive synthetic multiparallel corpus for Estonian translation and use it in training the models. The resulting models, training scripts and training data are released openly (to be made public upon de-anonymization).

Machine translation quality is highly reliant on large amounts of training data, and, when a limited amount of parallel data is available, synthetic back-translated or multilingual data can be used in addition. In this work, we introduce SynEst, a synthetic corpus of translations from 11 languages into Estonian which totals over 1 billion sentence pairs. Using this corpus, we investigate whether adding synthetic or English-centric additional data yields better translation quality for translation directions that do not include English. Our results show that while both strategies are effective, synthetic data gives better results. Our final models improve the performance of the baseline No Language Left Behind model while retaining its source-side multilinguality.

Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English.We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction.We show that MT models are not only capable of error correction out-of-the-box, but that they can also be fine-tuned to even better correction quality.Results show the effectiveness of this approach, with our multilingual model outperforming similar-sized mT5-based models and even competing favourably with larger models.

Machine translation (MT) research is most typically English-centric. In recent years, massively multilingual translation systems have also been increasingly popular. However, efforts purposefully focused on less-resourced languages are less widespread. In this paper, we focus on MT from and into the Estonian language. First, emphasizing the importance of data availability, we generate and publicly release a back-translation corpus of over 2 billion sentence pairs. Second, using these novel data, we create MT models covering 18 translation directions, all either from or into Estonian. We re-use the encoder of the NLLB multilingual model and train modular decoders separately for each language, surpassing the original NLLB quality. Our resulting MT models largely outperform other open-source MT systems, including previous Estonian-focused efforts, and are released as part of this submission.

We introduce SMUGRI-MT, an online neural machine translation system that covers 20 low-resource Finno-Ugric languages, along with seven high-resource languages.

In recent years, the quality of text-to-speech models has increased significantly, but most text-to-speech solutions are trained on datasets of read speech and do not cover conversational speaking styles due to the lack of suitable training data.This paper explores options for creating multi-style speech synthesis using speech recognition datasets that contain samples of spontaneous speech and dialogues but may also include background noise and an insufficient number of samples per speaker.We develop an Estonian multi-speaker TTS system that increases prosodic variability on conversational inputs while still being able to synthesize read speech.We show that our proposed approach can be used to train models that can be controlled to produce conversational speech with little compromise on audio quality.We also highlight a potential multilingual use case to achieve cross-lingual speaker and style transfer to lowresource languages that lack stylistically diverse speech corpora.

We present the MTee project - a research initiative funded via an Estonian public procurement to develop machine translation technology that is open-source and free of charge. The MTee project delivered an open-source platform serving state-of-the-art machine translation systems supporting four domains for six language pairs translating from Estonian into English, German, and Russian and vice-versa. The platform also features grammatical error correction and speech translation for Estonian and allows for formatted document translation and automatic domain detection. The software, data and training workflows for machine translation engines are all made publicly available for further use and research.

The work in progress on the CEF Action National Language Technology Platform (NLTP) is presented. The Action aims at combining the most advanced Language Technology (LT) tools and solutions in a new state-of-the-art, Artificial Intelli- gence (AI) driven, National Language Technology Platform (NLTP).

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domainsasdistinctlanguagesandusemultilingual NMT methods to create multi-domain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases.