Multi-domain Dialogue State Tracking

25 papers with code • 6 benchmarks • 2 datasets

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Most implemented papers

MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines

budzianowski/multiwoz LREC 2020

To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.

SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking


In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn.

Efficient Dialogue State Tracking by Selectively Overwriting Memory

clovaai/som-dst ACL 2020

This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.

Global-Locally Self-Attentive Dialogue State Tracker

salesforce/glad 19 May 2018

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.

Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

jojonki/MultiWOZ-Parser ACL 2018

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems.

Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems

jasonwu0731/trade-dst 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 7 Nov 2019

Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems.

Jointly Optimizing State Operation Prediction and Value Generation for Dialogue State Tracking

zengyan-97/Transformer-DST 24 Oct 2020

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.

Scalable Multi-Domain Dialogue State Tracking

google-research-datasets/simulated-dialogue 29 Dec 2017

We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.

Toward Scalable Neural Dialogue State Tracking Model

elnaaz/GCE-Model 3 Dec 2018

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.