  We investigate the problem of multi-domain Dialogue State Tracking (DST) with
open vocabulary. Existing approaches exploit BERT encoder and copy-based RNN
decoder, where the encoder predicts the state operation, and the decoder
generates new slot values. However, in such a stacked encoder-decoder
structure, the operation prediction objective only affects the BERT encoder and
the value generation objective mainly affects the RNN decoder. In this paper,
we propose a purely Transformer-based framework, where a single BERT works as
both the encoder and the decoder. In so doing, the operation prediction
objective and the value generation objective can jointly optimize this BERT for
DST. At the decoding step, we re-use the hidden states of the encoder in the
self-attention mechanism of the corresponding decoder layers to construct a
flat encoder-decoder architecture for effective parameter updating.
Experimental results show that our approach substantially outperforms the
existing state-of-the-art framework, and it also achieves very competitive
performance to the best ontology-based approaches.
