Dialogue State Tracking
125 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
Latest papers
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain.
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
ToLightUpTheSky/ParsingDST
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Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly.
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine-tuning.
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues.
Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation
A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation.
Diable: Efficient Dialogue State Tracking as Operations on Tables
amazon-science/efficient-dialogue-state-tracking-by-sequential-information-processing
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Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
AlibabaResearch/DAMO-ConvAI
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It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.
Continual Dialogue State Tracking via Example-Guided Question Answering
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
OLISIA: a Cascade System for Spoken Dialogue State Tracking
orange-opensource/olisia-dstc11
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Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language. In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model.
Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly.
