  Recently slot filling has witnessed great development thanks to deep learning
and the availability of large-scale annotated data. However, it poses a
critical challenge to handle a novel domain whose samples are never seen during
training. The recognition performance might be greatly degraded due to severe
domain shifts. Most prior works deal with this problem in a two-pass pipeline
manner based on metric learning. In practice, these dominant pipeline models
may be limited in computational efficiency and generalization capacity because
of non-parallel inference and context-free discrete label embeddings. To this
end, we re-examine the typical metric-based methods, and propose a new adaptive
end-to-end metric learning scheme for the challenging zero-shot slot filling.
Considering simplicity, efficiency and generalizability, we present a
cascade-style joint learning framework coupled with context-aware soft label
representations and slot-level contrastive representation learning to mitigate
the data and label shift problems effectively. Extensive experiments on public
benchmarks demonstrate the superiority of the proposed approach over a series
of competitive baselines.
