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Dialogue State Tracking

20 papers with code · Natural Language Processing
Subtask of Dialogue

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.

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Greatest papers with code

Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

NAACL 2018 google-research-datasets/simulated-dialogue

To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback.

DIALOGUE STATE TRACKING IMITATION LEARNING TASK-ORIENTED DIALOGUE SYSTEMS

Scalable Multi-Domain Dialogue State Tracking

29 Dec 2017google-research-datasets/simulated-dialogue

We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.

DIALOGUE STATE TRACKING TASK-ORIENTED DIALOGUE SYSTEMS TRANSFER LEARNING

Global-Locally Self-Attentive Dialogue State Tracker

19 May 2018salesforce/glad

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.

DIALOGUE STATE TRACKING TASK-ORIENTED DIALOGUE SYSTEMS

Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems

ACL 2019 jasonwu0731/trade-dst

Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.

DIALOGUE STATE TRACKING TASK-ORIENTED DIALOGUE SYSTEMS TRANSFER LEARNING

Toward Scalable Neural Dialogue State Tracking Model

3 Dec 2018budzianowski/multiwoz

The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance.

DIALOGUE STATE TRACKING

Fully Statistical Neural Belief Tracking

ACL 2018 nmrksic/neural-belief-tracker

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).

DIALOGUE MANAGEMENT DIALOGUE STATE TRACKING SPOKEN LANGUAGE UNDERSTANDING WORD EMBEDDINGS

Fully Statistical Neural Belief Tracking

29 May 2018nmrksic/neural-belief-tracker

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).

DIALOGUE STATE TRACKING

Counter-fitting Word Vectors to Linguistic Constraints

NAACL 2016 nmrksic/counter-fitting

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.

DIALOGUE STATE TRACKING SEMANTIC TEXTUAL SIMILARITY

Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset

12 Sep 2019google-research-datasets/dstc8-schema-guided-dialogue

This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data.

DIALOGUE STATE TRACKING SLOT FILLING

Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

1 Jun 2017nmrksic/attract-repel

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.

DIALOGUE STATE TRACKING SEMANTIC TEXTUAL SIMILARITY