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
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue.
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance.
Amendable Generation for Dialogue State Tracking
PaddlePaddle/Knover
•
•
EMNLP (NLP4ConvAI) 2021
In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.
Robustness through Data Augmentation Loss Consistency
optimization-for-data-driven-science/dair
•
•
Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
chiahsuan156/dst-as-prompting
•
•
EMNLP 2021
Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots.
Effective Sequence-to-Sequence Dialogue State Tracking
We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model.
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking
linweizhedragon/knowledge-aware-graph-enhanced-gpt-2-for-dialogue-state-tracking
•
•
EMNLP 2021
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances.
A Sequence-to-Sequence Approach to Dialogue State Tracking
sweetalyssum/Seq2Seq-DU
•
•
ACL 2021
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.
