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Noise-robust Named Entity Understanding for Virtual Assistants
Named Entity Understanding (NEU) plays an essential role in interactions
between users and voice assistants, since successfully identifying entities and
correctly linking them to their standard forms is crucial to understanding the
user's intent. NEU is a challenging task in voice assistants due to the
ambiguous nature of natural language and because noise introduced by speech
transcription and user errors occur frequently in spoken natural language
queries. In this paper, we propose an architecture with novel features that
jointly solves the recognition of named entities (a.k.a. Named Entity
Recognition, or NER) and the resolution to their canonical forms (a.k.a. Entity
Linking, or EL). We show that by combining NER and EL information in a joint
reranking module, our proposed framework improves accuracy in both tasks. This
improved performance and the features that enable it, also lead to better
accuracy in downstream tasks, such as domain classification and semantic
parsing.Comment: 9 page