Noise Robust Named Entity Understanding for Voice Assistants
Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang,, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek, Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan, Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal
TL;DR
This paper introduces a joint reranking architecture for NER and EL in voice assistants, significantly improving accuracy despite challenges posed by spoken queries.
Contribution
A novel joint reranking framework that enhances NER and EL performance in voice assistant applications.
Findings
NER accuracy improved by up to 3.13%
EL accuracy improved by up to 3.6%
Better performance in domain classification and semantic parsing
Abstract
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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