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

TL;DR

This paper introduces ViGGO, a new, clean, and diverse video game dialogue corpus designed for open-domain data-to-text generation, addressing limitations of existing datasets in diversity and noise.

Contribution

The paper presents ViGGO, a novel 7,000-sample corpus for open-domain dialogue, focusing on video game conversations with multiple dialogue act types, enhancing diversity and applicability.

Findings

Corpus is clean despite crowdsourcing

Includes 9 generalizable dialogue act types

Supports rich, open-domain conversations

Abstract

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.