Papers › Multi-Granularity Representations of Dialog
Multi-Granularity Representations of Dialog
Shikib Mehri, Maxine Eskenazi
Neural models of dialog rely on generalized latent representations of language. This paper introduces a novel training procedure which explicitly learns multiple representations of language at several levels of granularity. The multi-granularity training algorithm modifies the mechanism by which negative candidate responses are sampled in order to control the granularity of learned latent representations. Strong performance gains are observed on the next utterance retrieval task using both the MultiWOZ dataset and the Ubuntu dialog corpus. Analysis significantly demonstrates that multiple granularities of representation are being learned, and that multi-granularity training facilitates better transfer to downstream tasks.
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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) | DAM-MG | R10@1 | 0.753 | #20 of 25 | Archive leaderboard | report |
| Conversational Response Selection | Ubuntu Dialogue (v1, Ranking) | DAM-MG | R2@1 | 0.935 | #20 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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