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.

Abstract. This paper presents an exploitation of the lexicon of verb valencies for the Czech language named VerbaLex. The VerbaLex lexicon format, called complex valency frames, comprehends all the information found in three independent electronic dictionaries of verb valency frames and it is intensively linked to the Czech WordNet semantic network. The NLP laboratory at FI MU Brno develops a deep syntactic analyzer of Czech sentences, the parsing system synt. The system is based on an efficient and fast head-driven chart parsing algorithm. We present the latest results of using the information contained in the VerbaLex lexicon as one of the language specific features used in the tree ranking algorithm for the Best Analysis Selection algorithm, which is a crucial part of the syntactic analyser of free word order languages.

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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 pretrained on a large Czech corpus. We believe this data will support endeavours both of search relevance and multilingualfocused research communities.

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