Papers › The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn...
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
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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Tasks
1 archive task tag without a task page not shown.
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conversational Response Selection | Ubuntu Dialogue (v1, Ranking) | Dual-LSTM | R10@1 | 0.604 | #25 of 25 | Archive leaderboard | report |
| Conversational Response Selection | Ubuntu Dialogue (v1, Ranking) | Dual-LSTM | R10@2 | 0.745 | #25 of 25 | Archive leaderboard | report |
| Conversational Response Selection | Ubuntu Dialogue (v1, Ranking) | Dual-LSTM | R10@5 | 0.926 | #25 of 25 | Archive leaderboard | report |
| Conversational Response Selection | Ubuntu Dialogue (v1, Ranking) | Dual-LSTM | R2@1 | 0.878 | #25 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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