Web search engines focus on serving highly relevant results within hundreds of milliseconds. Pre-trained language transformer models such as BERT are therefore hard to use in this scenario due to their high computational demands. We present our real-time approach to the document ranking problem leveraging a BERT-based siamese architecture. The model is already deployed in a commercial search engine and it improves production performance by more than 3%. For further research and evaluation, we release DaReCzech, a unique data set of 1.6 million Czech user query-document pairs with manually assigned relevance levels. We also release Small-E-Czech, an Electra-small language model pre-trained on a large Czech corpus. We believe this data will support endeavours both of search relevance and multilingual-focused research communities.

Deep neural networks are known to be sensitive to adversarial examples - inputs that are created in such a way that they are similar (if viewed by people) to clean inputs, but the neural network has high confidence that they belong to another class. In this paper, we study a new type of neural network unit similar to the convolutional units, but with a more local behavior. The unit is based on the Gaussian radial basis function. We show that if we replace the first convolutional layer in a convolutional network by the new layer (called RBFolutional), we obtain better robustness towards adversarial samples on the MNIST and CIFAR10 datasets, without sacrificing the performance on the clean examples.

Blending of search results from several vertical sources became standard among web search engines. Similar scenarios appear in computational advertising, news recommendation, and other interactive systems. As such environments give only partial feedback, the evaluation of new policies conventionally requires expensive online A/B tests. Counterfactual approach is a promising alternative, nevertheless, it requires specific conditions for a valid off-policy evaluation. We release a large-scale, real-world vertical-blending dataset gathered bySeznam.cz web search engine. The dataset contains logged partial feedback with the corresponding propensity and is thus suited for counterfactual evaluation. We provide basic checks for validity and evaluate several learning methods.