The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
This provides a unique resource for researchers in dialogue systems, enabling development of neural models using unlabeled data, though it is incremental as it builds on existing dataset concepts.
The paper tackles the lack of large-scale datasets for unstructured multi-turn dialogue research by introducing the Ubuntu Dialogue Corpus, containing almost 1 million dialogues with over 7 million utterances, and provides benchmark performance for next-response selection tasks.
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.