  Tracking the state of the conversation is a central component in
task-oriented spoken dialogue systems. One such approach for tracking the
dialogue state is slot carryover, where a model makes a binary decision if a
slot from the context is relevant to the current turn. Previous work on the
slot carryover task used models that made independent decisions for each slot.
A close analysis of the results show that this approach results in poor
performance over longer context dialogues. In this paper, we propose to jointly
model the slots. We propose two neural network architectures, one based on
pointer networks that incorporate slot ordering information, and the other
based on transformer networks that uses self attention mechanism to model the
slot interdependencies. Our experiments on an internal dialogue benchmark
dataset and on the public DSTC2 dataset demonstrate that our proposed models
are able to resolve longer distance slot references and are able to achieve
competitive performance.
