Dialogue State Tracking
127 papers with code • 7 benchmarks • 11 datasets
Dialogue state tacking consists of determining at each turn of a dialogue the full representation of what the user wants at that point in the dialogue, which contains a goal constraint, a set of requested slots, and the user's dialogue act.
Benchmarks
These leaderboards are used to track progress in Dialogue State Tracking
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Libraries
Use these libraries to find Dialogue State Tracking models and implementationsDatasets
MultiWOZ
SGD
CoSQL
Dialogue State Tracking Challenge
Wizard-of-Oz
CrossWOZ
KLUE
Taskmaster-1
SIMMC2.0
Most implemented papers
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.
Global-Locally Self-Attentive Dialogue State Tracker
salesforce/glad
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Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
AuCson/SEDST
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However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
jasonwu0731/trade-dst
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ACL 2019
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
alexa/dstqa
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Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems.
Schema-Guided Dialogue State Tracking Task at DSTC8
google-research-datasets/dstc8-schema-guided-dialogue
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The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
thu-coai/CrossWOZ
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TACL 2020
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
salesforce/coco-dst
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ICLR 2021
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.
Jointly Optimizing State Operation Prediction and Value Generation for Dialogue State Tracking
zengyan-97/Transformer-DST
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However, in such a stacked encoder-decoder structure, the operation prediction objective only affects the BERT encoder and the value generation objective mainly affects the RNN decoder.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
huybery/r2sql
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Semantic parsing has long been a fundamental problem in natural language processing.
