Easy contextual intent prediction and slot detection

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How researchers see this paper

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Towards Open Intent Discovery for Conversational Text (2019, background): “Progress in the field of deep learning has led to the emergence of models that can detect user intents [2, 5, 6, 11, 14, 22, 24, 31, 47–49, 52] and identify semantically relevant entities linked with those intents (slot filling).”
The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components (2017, background): “LU models are built in a contextual manner to solve this problem [15], [22]–[24].”
SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding (2021, background): “SLU has been widely employed in various areas such as intent understanding (Tur and De Mori, 2011; Bhargava et al., 2013; Ravuri and Stolcke, 2015; Lugosch et al., 2019), question answering (Lee et al., 2018; Chuang et al., 2020), and sentiment analysis (Zadeh et al., 2018).”
Efficient Large-Scale Domain Classification with Personalized Attention (2018, unknown): “, 2017d,c) and contextual signals (Bhargava et al., 2013; Chen et al., 2016b; Hori et al., 2016; Kim et al., 2017a).”