  Dialogue state tracking (DST) is an essential component in task-oriented
dialogue systems, which estimates user goals at every dialogue turn. However,
most previous approaches usually suffer from the following problems. Many
discriminative models, especially end-to-end (E2E) models, are difficult to
extract unknown values that are not in the candidate ontology; previous
generative models, which can extract unknown values from utterances, degrade
the performance due to ignoring the semantic information of pre-defined
ontology. Besides, previous generative models usually need a hand-crafted list
to normalize the generated values. How to integrate the semantic information of
pre-defined ontology and dialogue text (heterogeneous texts) to generate
unknown values and improve performance becomes a severe challenge. In this
paper, we propose a Copy-Enhanced Heterogeneous Information Learning model with
multiple encoder-decoder for DST (CEDST), which can effectively generate all
possible values including unknown values by copying values from heterogeneous
texts. Meanwhile, CEDST can effectively decompose the large state space into
several small state spaces through multi-encoder, and employ multi-decoder to
make full use of the reduced spaces to generate values. Multi-encoder-decoder
architecture can significantly improve performance. Experiments show that CEDST
can achieve state-of-the-art results on two datasets and our constructed
datasets with many unknown values.
