Multi-domain Dialogue State Tracking

29 papers with code • 6 benchmarks • 2 datasets

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Libraries

Use these libraries to find Multi-domain Dialogue State Tracking models and implementations

Most implemented papers

Scalable Multi-Domain Dialogue State Tracking

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

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.

Toward Scalable Neural Dialogue State Tracking Model

elnaaz/GCE-Model 3 Dec 2018

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.

Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

renll/ComerNet IJCNLP 2019

Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.

Non-Autoregressive Dialog State Tracking

henryhungle/NADST ICLR 2020

Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself.

Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

stanford-oval/zero-shot-multiwoz-acl2020 ACL 2020

We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2. 1 dataset.

A Simple Language Model for Task-Oriented Dialogue

salesforce/simpletod NeurIPS 2020

Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

BDBC-KG-NLP/PIN_EMNLP2020 EMNLP 2020

In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

zlinao/MinTL EMNLP 2020

In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data.

DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

alexa/dialoglue 28 Sep 2020

A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains.