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Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
[article]
2019
arXiv
pre-print
Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task
arXiv:1803.11326v4
fatcat:ivmfc6lo4nbzzcwlf6biedhmje