We present a new release of the Czech-English parallel corpus CzEng. CzEng 1.6 consists of about 0.5 billion words ("gigaword") in each language. The corpus is equipped with automatic annotation at a deep syntactic level of representation and alternatively in Universal Dependencies. Additionally, we release the complete annotation pipeline as a virtual machine in the Docker virtualization toolkit.

This paper describes the phrase-based systems jointly submitted by CUNI and LMU to English-Czech and English-Romanian News translation tasks of WMT16. In contrast to previous years, we strictly limited our training data to the constraint datasets, to allow for a reliable comparison with other research systems. We experiment with using several additional models in our system, including a feature-rich discriminative model of phrasal translation.

This paper describes the neural and phrase-based machine translation systems submitted by CUNI to English-Czech News Translation Task of WMT17. We experiment with synthetic data for training and try several system combination techniques, both neural and phrase-based. Our primary submission CU-CHIMERA ends up being phrase-based backbone which incorporates neural and deep-syntactic candidate translations.

We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the lowresource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a "parent model" for the high-resource language pair followed by adaptation on the related lowresource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.

We describe our submission to the ITdomain translation task of WMT 2016. We perform domain adaptation with dictionary data on already trained MT systems with no further retraining. We apply our approach to two conceptually different systems developed within the QTLeap project: TectoMT and Moses, as well as Chimera, their combination. In all settings, our method improves the translation quality. Moreover, the basic variant of our approach is applicable to any MT system, including a black-box one.