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The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human judges, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (translation adequacy). While human translation is still rated as more fluent, CUBBITT is shown to be substantially more fluent than previous state-of-the-art systems. Moreover, most participants of a Translation Turing test struggle to distinguish CUBBITT translations from human translations. This work approaches the quality of human translation and even surpasses it in adequacy in certain circumstances.This suggests that deep learning may have the potential to replace humans in applications where conservation of meaning is the primary aim. The quality of human language translation has been thought to be unattainable by computer translation systems. Here the authors present CUBBITT, a deep learning system that outperforms professional human translators in retaining text meaning in English-to-Czech news translation, and validate the system on English-French and English-Polish language pairs.

This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model ( ). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra “more data and larger models”, we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints.

A linguistically informed distant reading presupposes an adequate performance of Natural Language Processing tools. This article describes our evaluation of the UDPipe parser on a manually annotated sample of nineteenth-century Czech poetry in the following steps: (1) creation of a documented data set for this domain (poetry, nineteenth century, Czech); (2) domain-specific annotation decisions; (3) error analysis. The sample consisted of 29 randomly selected poems which were first automatically tagged and parsed with the UDPipe parser and then manually checked word by word. The following features were checked: word segmentation (chunking), lemmatization, part of speech assignment, assignment of more fine-grained morphological details, the position in the syntactic dependency tree (selection of the syntactic parent), as well as the label of the syntactic relation between the word and its parent. The findings were analyzed. The most typical parser errors are associated with complex noun phrases that contain other noun(s) as modifier(s), especially when these occur in a poetry-specific word order, that is, preposed to the governing noun. On the other hand, neither archaic orthography nor neologisms posed substantial issues.

We present HamleDT—a HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. In the present article, we provide a thorough investigation and discussion of a number of phenomena that are comparable across languages, though their annotation in treebanks often differs. We claim that transformation procedures can be designed to automatically identify most such phenomena and convert them to a unified annotation style. This unification is beneficial both to comparative corpus linguistics and to machine learning of syntactic parsing.

The tool described in this article has been designed to help MT developers by implementing a web-based graphical user interface that allows to systematically compare and evaluate various MT engines/experiments using comparative analysis via automatic measures and statistics. The evaluation panel provides graphs, tests for statistical significance and n-gram statistics. We also present a demo server with WMT14 and WMT15 translations.

The present paper summarizes our recent results concerning English-Czech Machine Translation implemented in the TectoMT framework. The system uses tectogrammatical trees as the transfer medium. A detailed analysis of errors made by the previous version of the system (considered as the baseline) is presented first. Then several improvements of the system are described that led to better translation quality in terms of BLEU and NIST scores. The biggest performance gain comes from applying Hidden Tree Markov Model in the transfer phase, which is a novel technique in the field of Machine Translation. Adapted from the source document

This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra \"more data and larger models\", we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints.

Improving English-Czech Tectogrammatical MT The present paper summarizes our recent results concerning English-Czech Machine Translation implemented in the TectoMT framework. The system uses tectogrammatical trees as the transfer medium. A detailed analysis of errors made by the previous version of the system (considered as the baseline) is presented first. Then several improvements of the system are described that led to better translation quality in terms of BLEU and NIST scores. The biggest performance gain comes from applying Hidden Tree Markov Model in the transfer phase, which is a novel technique in the field of Machine Translation.

We present the CUNI-Bergamot submission for the WMT22 General translation task. We compete in English\\(\\rightarrow\\)Czech direction. Our submission further explores block backtranslation techniques. Compared to the previous work, we measure performance in terms of COMET score and named entities translation accuracy. We evaluate performance of MBR decoding compared to traditional mixed backtranslation training and we show a possible synergy when using both of the techniques simultaneously. The results show that both approaches are effective means of improving translation quality and they yield even better results when combined.