REVIEW 4 cited by
Toward Scalable Neural Dialogue State Tracking Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.
Forward citations
Cited by 4 Pith papers
-
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
COMER generates belief states hierarchically with a shared sequence decoder, achieving 48.79% joint goal accuracy on MultiWOZ and near-state-of-the-art on WoZ2.0, while claiming O(1) inference time relative to the pre...
-
Dialog State Tracking with Reinforced Data Augmentation
A reinforcement learning based data augmentation framework for dialog state tracking that learns which paraphrase replacements are useful and improves joint goal accuracy on WoZ and MultiWoZ (restaurant).
-
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
A multi-teacher, single-student distillation framework lets a multi-domain dialogue generator skip an external belief tracker at inference while still exploiting manually labeled dialogue states during training.
-
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling
Adding a slot-presence classifier head to generative LLM dialogue state trackers improves joint goal accuracy by about 3% absolute and allows detected errors to be self-corrected or confirmed by the user.
Discussion (0). Continue with ORCID to comment.