Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
Yuanjun Shi, Linzhi Wu, Minglai Shao · 2023
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 contextfree 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 contextaware 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.1