Multi-domain Dialogue State Tracking
29 papers with code • 6 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Multi-domain Dialogue State Tracking
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Multi-domain Dialogue State Tracking models and implementations
MultiWOZ
SGD
Latest papers with no code
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs.
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.
Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks
For multi-domain DST, the data sparsity problem is also a major obstacle due to the increased number of state candidates.
Leveraging External Knowledge for Out-Of-Vocabulary Entity Labeling
This network projects the slot into an attribute space derived from the KB, and, by leveraging similarities in this space, we propose candidate slot keys and values to the dialogue state tracker.
Dialog State Tracking: A Neural Reading Comprehension Approach
In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation.
HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking
In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type.
Learning to Memorize in Neural Task-Oriented Dialogue Systems
Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism.
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
We present a novel approach to dialogue state tracking and referring expression resolution tasks.
