Papers › Pretraining the Noisy Channel Model for Task-Oriented Dialogue
Pretraining the Noisy Channel Model for Task-Oriented Dialogue
Qi Liu, Lei Yu, Laura Rimell, Phil Blunsom
Direct decoding for task-oriented dialogue is known to suffer from the explaining-away effect, manifested in models that prefer short and generic responses. Here we argue for the use of Bayes' theorem to factorize the dialogue task into two models, the distribution of the context given the response, and the prior for the response itself. This approach, an instantiation of the noisy channel model, both mitigates the explaining-away effect and allows the principled incorporation of large pretrained models for the response prior. We present extensive experiments showing that a noisy channel model decodes better responses compared to direct decoding and that a two stage pretraining strategy, employing both open-domain and task-oriented dialogue data, improves over randomly initialized models.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| End-To-End Dialogue Modelling | MULTIWOZ 2.0 | Noisy Channel Model | BLEU | 20.6 | #2 of 6 | Archive leaderboard | report |
| End-To-End Dialogue Modelling | MULTIWOZ 2.0 | Noisy Channel Model | MultiWOZ (Inform) | 86.9 | #2 of 6 | Archive leaderboard | report |
| End-To-End Dialogue Modelling | MULTIWOZ 2.0 | Noisy Channel Model | MultiWOZ (Success) | 76.2 | #2 of 6 | 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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