We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question answering. In particular, since summarization and question answering lack benchmark datasets for the Polish language, we describe in detail their construction and make them publicly available. Additionally, we present plT5 - a general-purpose text-to-text model for Polish that can be fine-tuned on various Natural Language Processing (NLP) tasks with a single training objective. Unsupervised denoising pre-training is performed efficiently by initializing the model weights with a multi-lingual T5 (mT5) counterpart. We evaluate the performance of plT5, mT5, Polish BART (plBART), and Polish GPT-2 (papuGaPT2). The plT5 scores top on all of these tasks except summarization, where plBART is best. In general (except summarization), the larger the model, the better the results. The encoder-decoder architectures prove to be better than the decoder-only equivalent.

We submitted two uni-directional models, one for English→Icelandic direction and other for Icelandic→English direction. Our news translation system is based on the transformer-big architecture, it makes use of corpora filtering, back-translation and forward translation applied to parallel and monolingual data alike

This paper describes the submission to the WMT20 shared news translation task by Samsung R&D Institute Poland. We submitted systems for six language directions: English to Czech, Czech to English, English to Polish, Polish to English, English to Inuktitut and Inuktitut to English. For each, we trained a single-direction model. However, directions including English, Polish and Czech were derived from a common multilingual base, which was later fine-tuned on each particular direction. For all the translation directions, we used a similar training regime, with iterative training corpora improvement through back-translation and model ensembling. For the En → Cs direction, we additionally leveraged document-level information by re-ranking the beam output with a separate model.