Robust Zero-Shot Cross-Domain Slot Filling with Example Values

An increasing number of task-oriented dia-logue systems now rely on deep learning-based slot filling models, usually needing largeamounts of labeled training data for the targetdomain. However, often, either little to no tar-get domain training data is available, or thetraining and target domain schemas are mis-aligned, as is common for web forms on simi-lar websites. Prior approaches to zero-shot slotfilling use slot descriptions to learn concepts,which are not robust to misaligned schemas.In this work, we propose utilizing both theslot description and a small number of exam-ples of slot values, which may be easily avail-able, to learn semantic representations of slotswhich are transferable across domains and ro-bust to misaligned schemas. Our experimentsshow improved slot filling performance overstate-of-the-art models on two multi-domaindatasets, in the regular and low-data settings.

• Natural Language Processing