Papers › Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
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
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-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 |
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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