The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

WS 2015  ·  Ryan Lowe, Nissan Pow, Iulian Serban, Joelle Pineau ·

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.

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Datasets


Introduced in the Paper:

UDC

Used in the Paper:

Dialogue State Tracking Challenge
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) Dual-LSTM R10@1 0.604 # 22
R10@2 0.745 # 21
R10@5 0.926 # 21
R2@1 0.878 # 14

Methods


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