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Noise Robust Named Entity Understanding for Voice Assistants

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arxiv 2005.14408 v3 pith:3W544QZB submitted 2020-05-29 cs.CL cs.AIcs.LG

Deepak Muralidharan , Joel Ruben Antony Moniz , Sida Gao , Xiao Yang , Justine Kao , Stephen Pulman , Atish Kothari , Ray Shen
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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 Roger Zheng Peter Grasch Jason D. Williams Lin Li
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classification cs.CLcs.AIcs.LG
keywords entityaccuracynamedtasksunderstandingvoiceaccuraciesarchitecture
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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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