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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 MULTI-DOMAIN DIALOGUE STATE TRACKING TASK-ORIENTED DIALOGUE SYSTEMS TRANSFER LEARNING

Efficient Dialogue State Tracking by Selectively Overwriting Memory

ACL 2020 clovaai/som-dst

This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.

DIALOGUE STATE TRACKING MULTI-DOMAIN DIALOGUE STATE TRACKING

Non-Autoregressive Dialog State Tracking

ICLR 2020 henryhungle/NADST

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.

DIALOGUE STATE TRACKING MULTI-DOMAIN DIALOGUE STATE TRACKING

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

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

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.

DATA AUGMENTATION DIALOGUE STATE TRACKING MULTI-DOMAIN DIALOGUE STATE TRACKING TRANSFER LEARNING ZERO-SHOT LEARNING

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

EMNLP 2020 BDBC-KG-NLP/PIN_EMNLP2020

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.

DIALOGUE STATE TRACKING MULTI-DOMAIN DIALOGUE STATE TRACKING