Abstract
Over-dependence on domain ontology and
lack of knowledge sharing across domains are
two practical and yet less studied problems of
dialogue state tracking. Existing approaches
generally fall short in tracking unknown slot
values during inference and often have diffi-
culties in adapting to new domains. In this
paper, we propose a TRAnsferable Dialogue
statE generator (TRADE) that generates dialogue states from utterances using a copy
mechanism, facilitating knowledge transfer
when predicting (domain, slot, value) triplets
not encountered during training. Our model is
composed of an utterance encoder, a slot gate,
and a state generator, which are shared across
domains. Empirical results demonstrate that
TRADE achieves state-of-the-art joint goal accuracy of 48.62% for the five domains of MultiWOZ, a human-human dialogue dataset. In
addition, we show its transferring ability by
simulating zero-shot and few-shot dialogue
state tracking for unseen domains. TRADE
achieves 60.58% joint goal accuracy in one of
the zero-shot domains, and is able to adapt
to few-shot cases without forgetting already
trained domains