Task-Oriented Dialogue Systems
117 papers with code • 4 benchmarks • 19 datasets
Achieving a pre-defined task through a dialog.
Benchmarks
These leaderboards are used to track progress in Task-Oriented Dialogue Systems
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Libraries
Use these libraries to find Task-Oriented Dialogue Systems models and implementations
MultiWOZ
SGD
MuTual
CrossWOZ
ABCD
BiToD
Latest papers with no code
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems
In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection.
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition
We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems.
RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education
RECIPE4U includes comprehensive records of these interactions, including conversation logs, students' intent, students' self-rated satisfaction, and students' essay edit histories.
Can Similarity-Based Domain-Ordering Reduce Catastrophic Forgetting for Intent Recognition?
While existing dialogue systems research has explored replay-based and regularization-based methods to this end, the effect of domain ordering on the CL performance of intent recognition models remains unexplored.
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems
Notably, we have observed that fine-tuning enhances the simulator's coherence with user goals, effectively mitigating hallucinations -- a major source of inconsistencies in simulator responses.
Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations
This review gives an extensive overview of evaluation methods for task-oriented dialogue systems, paying special attention to practical applications of dialogue systems, for example for customer service.
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models.
LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems
Linguistic entrainment, or alignment, represents a phenomenon where linguistic patterns employed by conversational participants converge to one another.
Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems
In this paper, we propose a diagnosis method to attribute bias to each component of a TOD system.
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
Tracking dialogue states is an essential topic in task-oriented dialogue systems, which involve filling in the necessary information in pre-defined slots corresponding to a schema.
