|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.
Ranked #6 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #6 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
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.
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.
Ranked #4 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1
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.
Ranked #5 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems.
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.
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.