Papers › Neural Generation for Czech: Data and Baselines

Neural Generation for Czech: Data and Baselines

1 Oct 2019WS 2019 10archive 2025-07-28

Ond{\v{r}}ej Du{\v{s}}ek, Filip Jur{\v{c}}{\'\i}{\v{c}}ek

We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.

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