Abstract
Task-oriented dialog systems increasingly rely
on deep learning-based slot filling models,
usually needing extensive labeled training data
for target domains. Often, however, little to no
target domain training data may be available,
or the training and target domain schemas may
be misaligned, as is common for web forms
on similar websites. Prior zero-shot slot filling
models use slot descriptions to learn concepts,
but are not robust to misaligned schemas. We
propose utilizing both the slot description and
a small number of examples of slot values,
which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned
schemas. Our approach outperforms state-ofthe-art models on two multi-domain datasets,
especially in the low-data setting