ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
This provides a cleaner and more versatile dataset for training natural language generation models in open-domain conversations, though it is incremental as it builds on existing data-to-text efforts.
The authors tackled the lack of diverse and clean data for open-domain dialogue systems by creating ViGGO, a new corpus of 7K samples in the video game domain, which features conversational dialogue acts and is designed to reduce noise compared to existing datasets.
The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.