This paper investigates how to combine encoders and decoders of different independently trained NMT models. Combining encoders/decoders is not directly possible since the intermediate representations of any two independent NMT models are different and cannot be combined without modification. To address this, firstly, a dimension adapter is added if the encoder and decoder have different embedding dimensionalities, and secondly, representation adapter layers are added to align the encoder’s representations for the decoder to process. As a proof of concept, this paper looks at many-to-Estonian translation and combines a massively multilingual encoder (NLLB) and a high-quality language-specific decoder. The paper successfully demonstrates that the sentence representations of two independent NMT models can be made compatible without changing the pre-trained components while keeping translation quality from deteriorating. Results show improvements in both translation quality and speed for many-to-one translation over the baseline multilingual model.

This paper explores cost-efficient methods to adapt pretrained Large Language Models (LLMs) to new lower-resource languages, with a specific focus on Estonian. Leveraging the Llama 2 model, we investigate the impact of combining cross-lingual instruction-tuning with additional monolingual pretraining. Our results demonstrate that even a relatively small amount of additional monolingual pretraining followed by cross-lingual instruction-tuning significantly enhances results on Estonian. Furthermore, we showcase cross-lingual knowledge transfer from high-quality English instructions to Estonian, resulting in improvements in commonsense reasoning and multi-turn conversation capabilities. Our best model, named Llammas, represents the first open-source instruction-following LLM for Estonian. Additionally, we publish Alpaca-est, the first general task instruction dataset for Estonia. These contributions mark the initial progress in the direction of developing open-source LLMs for Estonian.

This study explores enhancing grammatical error correction (GEC) through automatic error generation (AEG) using language models (LMs). Specifically, we fine-tune Llama 2 LMs for error generation and find that this approach yields synthetic errors akin to human errors. Next, we train GEC Llama models using these artificial errors and outperform previous state-of-the-art error correction models, with gains ranging between 0.8 and 6 F0.5 points across all tested languages (German, Ukrainian, and Estonian). Moreover, we demonstrate that generating errors by fine-tuning smaller sequence-to-sequence models and prompting large commercial LMs (GPT3.5 and GPT4) also results in synthetic errors beneficially affecting error generation models. We openly release trained models for error generation and correction as well as all the synthesized error datasets for the covered languages.

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

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.

Large multilingual Transformer-based machine translation models have had a pivotal role in making translation systems available for hundreds of languages with good zero-shot translation performance. One such example is the universal model with shared encoder-decoder architecture. Additionally, jointly trained language-specific encoder-decoder systems have been proposed for multilingual neural machine translation (NMT) models. This work investigates various knowledge-sharing approaches on the encoder side while keeping the decoder language- or language-group-specific. We propose a novel approach, where we use universal, language-group-specific and language-specific modules to solve the shortcomings of both the universal models and models with language-specific encoders-decoders. Experiments on a multilingual dataset set up to model real-world scenarios, including zero-shot and low-resource translation, show that our proposed models achieve higher translation quality compared to purely universal and language-specific approaches.

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.

In recent years, large multilingual pre-trained neural machine translation model research has grown and it is common for these models to be publicly available for usage and fine-tuning. Low-resource languages benefit from the pre-trained models, because of knowledge transfer from high- to medium-resource languages. The recently available M2M-100 model is our starting point for cross-lingual transfer learning to Finno-Ugric languages, like Livonian. We participate in the WMT22 General Machine Translation task, where we focus on the English-Livonian language pair. We leverage data from other Finno-Ugric languages and through that, we achieve high scores for English-Livonian translation directions. Overall, instead of training a model from scratch, we use transfer learning and back-translation as the main methods and fine-tune a publicly available pre-trained model. This in turn reduces the cost and duration of training high-quality multilingual neural machine translation models.