  Generalising dialogue state tracking (DST) to new data is especially
challenging due to the strong reliance on abundant and fine-grained supervision
during training. Sample sparsity, distributional shift and the occurrence of
new concepts and topics frequently lead to severe performance degradation
during inference. In this paper we propose a training strategy to build
extractive DST models without the need for fine-grained manual span labels. Two
novel input-level dropout methods mitigate the negative impact of sample
sparsity. We propose a new model architecture with a unified encoder that
supports value as well as slot independence by leveraging the attention
mechanism. We combine the strengths of triple copy strategy DST and value
matching to benefit from complementary predictions without violating the
principle of ontology independence. Our experiments demonstrate that an
extractive DST model can be trained without manual span labels. Our
architecture and training strategies improve robustness towards sample
sparsity, new concepts and topics, leading to state-of-the-art performance on a
range of benchmarks. We further highlight our model's ability to effectively
learn from non-dialogue data.
