Отслеживание состояния диалога
Related Papers
Dialog state tracking, a machine reading approach using Memory Network
AbstractIn an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced …...
Conversational Semantic Parsing for Dialog State Tracking
AbstractWe consider a new perspective on dialog state tracking (DST), the task of estimating a user’s goal through the course of a dialog. By formulating DST as a semantic parsing …...
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
AbstractDialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and ontology-free methods. An ontology-based …...
Out-of-Task Training for Dialog State Tracking Models
AbstractDialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by …...
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
AbstractRecent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach …...
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
AbstractTask-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures …...
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
AbstractAs the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, …...
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
AbstractZero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose …...
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
AbstractDialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with constantly changing services, DST …...
Continual Prompt Tuning for Dialog State Tracking
AbstractA desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually …...