Consensus-based fusion rules are derived for Gaussian mixture model priors, including a two-agent means-as-observations fusion that makes both agents' posterior estimates identical.
Noise Robust Named Entity Understanding for Voice Assistants
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Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.
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Sensor Fusion Methods for Gaussian Mixture Models
Consensus-based fusion rules are derived for Gaussian mixture model priors, including a two-agent means-as-observations fusion that makes both agents' posterior estimates identical.