Andre Tättar


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Multilingual Neural Machine Translation With the Right Amount of Sharing
Taido Purason | Andre Tättar
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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.

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MTee: Open Machine Translation Platform for Estonian Government
Toms Bergmanis | Marcis Pinnis | Roberts Rozis | Jānis Šlapiņš | Valters Šics | Berta Bernāne | Guntars Pužulis | Endijs Titomers | Andre Tättar | Taido Purason | Hele-Andra Kuulmets | Agnes Luhtaru | Liisa Rätsep | Maali Tars | Annika Laumets-Tättar | Mark Fishel
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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.


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Extremely low-resource machine translation for closely related languages
Maali Tars | Andre Tättar | Mark Fišel
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for Võro, North and South Saami and present first results of neural machine translation for these languages.


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A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
Rachel Bawden | Biao Zhang | Lisa Yankovskaya | Andre Tättar | Matt Post
Findings of the Association for Computational Linguistics: EMNLP 2020

We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU’s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.

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ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task
Rachel Bawden | Biao Zhang | Andre Tättar | Matt Post
Proceedings of the Fifth Conference on Machine Translation

We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.


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University of Tartu’s Multilingual Multi-domain WMT19 News Translation Shared Task Submission
Andre Tättar | Elizaveta Korotkova | Mark Fishel
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the University of Tartu’s submission to the news translation shared task of WMT19, where the core idea was to train a single multilingual system to cover several language pairs of the shared task and submit its results. We only used the constrained data from the shared task. We describe our approach and its results and discuss the technical issues we faced.

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Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings
Elizaveta Yankovskaya | Andre Tättar | Mark Fishel
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

We propose the use of pre-trained embeddings as features of a regression model for sentence-level quality estimation of machine translation. In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model. In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system. Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).


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Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings
Maksym Del | Andre Tättar | Mark Fishel
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the University of Tartu’s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.

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Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings
Elizaveta Yankovskaya | Andre Tättar | Mark Fishel
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone.


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bleu2vec: the Painfully Familiar Metric on Continuous Vector Space Steroids
Andre Tättar | Mark Fishel
Proceedings of the Second Conference on Machine Translation