Papers Multi-domain Dialogue State Tracking
“Multi-domain Dialogue State Tracking” 태그가 달린 논문 50편 · 필터 해제
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. While general pre-trained language mode…
Dialogue State TrackingGraph Neural NetworkLanguage ModelingLanguage Modelling+4Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention
As an important component of task-oriented dialogue systems, dialogue state tracking is designed to track the dialogue state through the conversations between users and systems. Multi-domain dialogue state tracking is a …
Dialogue State TrackingMulti-domain Dialogue State TrackingTask-Oriented Dialogue SystemsAct-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. Representations of dialogu…
Dialogue State TrackingMachine Reading ComprehensionMulti-domain Dialogue State TrackingReading Comprehension+1Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches th…
DecoderDialogue State TrackingMulti-domain Dialogue State TrackingTransfer LearningDialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this paper, we hypothesize that …
Abstractive Dialogue SummarizationDialogue State TrackingLanguage ModelingLanguage Modelling+1Know 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…
Dialogue State TrackingMulti-domain Dialogue State TrackingTransfer LearningDynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches th…
DecoderDialogue State TrackingMulti-domain Dialogue State TrackingTransfer LearningAmendable Generation for Dialogue State Tracking
In task-oriented dialogue systems, recent dialogue state tracking methods tend to perform one-pass generation of the dialogue state based on the previous dialogue state. The mistakes of these models made at the current t…
Dialogue State TrackingMulti-domain Dialogue State TrackingTask-Oriented Dialogue SystemsRobustness through Data Augmentation Loss Consistency
While deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution shifts or adversarial attacks. Synthetic d…
Multi-domain Dialogue State TrackingVisual Question AnsweringVisual Question Answering (VQA)"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. However, written dialogues are not sufficient to fully capture the nature of spoken conversations as well as th…
BenchmarkingDialogue State TrackingMulti-domain Dialogue State Trackingspeech-recognition+3Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Task-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-speci…
Dialogue State TrackingLanguage ModelingLanguage ModellingMulti-domain Dialogue State TrackingEffective Sequence-to-Sequence Dialogue State Tracking
Sequence-to-sequence models have been applied to a wide variety of NLP tasks, but how to properly use them for dialogue state tracking has not been systematically investigated. In this paper, we study this problem from t…
Dialogue State TrackingLanguage ModelingLanguage ModellingMulti-domain Dialogue State Tracking+1Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverag…
dialog state trackingDialogue State TrackingMulti-domain Dialogue State TrackingPreview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverag…
dialog state trackingMulti-domain Dialogue State TrackingKnowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representati…
Dialogue State TrackingGraph AttentionMulti-domain Dialogue State TrackingTask-Oriented Dialogue SystemsSCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge…
Dialogue State TrackingLanguage ModelingLanguage ModellingMulti-domain Dialogue State Tracking+3Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking
Dialogue state tracking for multi-domain dialogues is challenging because the model should be able to track dialogue states across multiple domains and slots. Past studies had its limitations in that they did not factor …
Dialogue State TrackingLanguage ModelingLanguage ModellingMulti-domain Dialogue State TrackingA Sequence-to-Sequence Approach to Dialogue State Tracking
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made r…
ClassificationDecoderDialogue State TrackingMulti-domain Dialogue State Tracking+1Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking
Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or …
Dialogue State TrackingMulti-domain Dialogue State TrackingGCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking
As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user …
Dialogue State TrackingMulti-domain Dialogue State TrackingTask-Oriented Dialogue Systems