ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants

Deepak Muralidharan, Joel Ruben Antony Moniz, Weicheng Zhang, Stephen Pulman, Lin Li, Megan Barnes, Jingjing Pan, Jason Williams, Alex Acero

Named entity recognition (NER) is usually developed and tested on text from well-written sources. However, in intelligent voice assistants, where NER is an important component, input to NER may be noisy because of user or speech recognition error. In applications, entity labels may change frequently, and non-textual properties like topicality or popularity may be needed to choose among alternatives.

We describe a NER system intended to address these problems. We test and train this system on a proprietary user-derived dataset. We compare with a baseline text-only NER system; the baseline enhanced with external gazetteers; and the baseline enhanced with the search and indirect labelling techniques we describe below. The final configuration gives around 6% reduction in NER error rate. We also show that this technique improves related tasks, such as semantic parsing, with an improvement of up to 5% in error rate.


doi: 10.21437/Interspeech.2021-1877

Cite as: Muralidharan, D., Moniz, J.R.A., Zhang, W., Pulman, S., Li, L., Barnes, M., Pan, J., Williams, J., Acero, A. (2021) DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants. Proc. Interspeech 2021, 1234-1238, doi: 10.21437/Interspeech.2021-1877

@inproceedings{muralidharan21_interspeech,
  author={Deepak Muralidharan and Joel Ruben Antony Moniz and Weicheng Zhang and Stephen Pulman and Lin Li and Megan Barnes and Jingjing Pan and Jason Williams and Alex Acero},
  title={{DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={1234--1238},
  doi={10.21437/Interspeech.2021-1877}
}