Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
Samuel Louvan, Bernardo Magnini · 2018
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system.Most approaches for this task rely solely on the domain-specific datasets for training.We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup.Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-theart.In particular, NER is effective when supervised at the lower layer of the model.For low-resource scenarios, we found that MTL is effective for one dataset.