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The uptake of deep learning in natural language generation (NLG) led to therelease of both small and relatively large parallel corpora for training neuralmodels. The existing data-to-text datasets are, however, aimed at task-orienteddialogue systems, and often thus limited in diversity and versatility. They aretypically crowdsourced, with much of the noise left in them. Moreover, currentneural NLG models do not take full advantage of large training data, and due totheir strong generalizing properties produce sentences that look template-likeregardless. We therefore present a new corpus of 7K samples, which (1) is cleandespite being crowdsourced, (2) has utterances of 9 generalizable andconversational dialogue act types, making it more suitable for open-domaindialogue systems, and (3) explores the domain of video games, which is new todialogue systems despite having excellent potential for supporting richconversations.

ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation