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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Generating text from structured data
 Trebuňa, František ; Rosa, Rudolf (advisor) ; Kasner, Zdeněk (referee)
In this thesis we examine ways of conditionally generating document-scale natural language text given structured input data. Specifically we train Deep Neural Network models on RotoWire dataset containing statistical data about basketball matches paired with descriptive summaries. First, we analyse the dataset and propose several prepro- cessing methods (e.g. Byte Pair Encoding). Next, we train a baseline model based on the Encoder-Decoder architecture on the preprocessed dataset. We discuss several prob- lems of the baseline and explore advanced Deep Neural Network architectures that aim to solve them (Copy attention, Content Selection, Content Planning). We hypothesize that our models are not able to learn the structure of the input data and we propose a method reducing its complexity. Our best model trained on the simplified data manages to outperform the baseline by more than 5 BLEU points. 1
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