Papers › Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

13 Oct 2015arXiv:1510.03753archive 2025-07-28

Rudolf Kadlec, Martin Schmid, Jan Kleindienst

This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset. Finally, we discuss our future plans using this corpus.

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Tasks

Conversational Response SelectionDeep Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) Dual-BiLSTM R10@1 0.630 #24 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) Dual-BiLSTM R10@2 0.780 #24 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) Dual-BiLSTM R10@5 0.944 #24 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) Dual-BiLSTM R2@1 0.895 #24 of 25 Archive leaderboard report

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