Joint semantic utterance classification and slot filling with recursive neural networks

By Daniel Guo, Gökhan Tür, Wen-tau Yih, Geoffry Zweig. 2014

169 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “Spoken language understanding (SLU) systems play a vital role in ubiquitous artificially intelligent voice-enabled personal assistants.”
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling (2016, methodology): “Joint model for intent detection and slot filling has also been proposed in literature [8, 9].”
Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM (2016, background): “One notable extension is the use of recursive neural networks, framing the problem as semantic parsing [22].”
User Information Augmented Semantic Frame Parsing Using Progressive Neural Networks (2018, methodology): “Recent research focuses on the joint model to learn two tasks together [9, 10, 11, 2].”
Survey on evaluation methods for dialogue systems (2019, methodology): “Recent methods propose to jointly learn the tasks of intent identification and concept detection (Guo et al. 2014; Zhang and Wang 2016).”
FLIN: A Flexible Natural Language Interface for Web Navigation (2020, unknown): “Models that jointly perform intent detection and slot filling (Guo et al., 2014; Liu and Lane, 2016; Chen et al., 2019) are not applicable to our problem for three reasons.”
ONENET: Joint domain, intent, slot prediction for spoken language understanding (2017, methodology): “50 4 [16] 91.”
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (2018, methodology): “…approaches usually suffer from error propagation due to their independent models, the joint model for slot ﬁlling and intent detection has been proposed to improve sentence-level semantics via mutual enhancement between two tasks (Guo et al., 2014; Hakkani-T¨ur et al., 2016; Chen et al., 2016).”
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (2018, methodology): “4% (Guo et al., 2014) Joint model with recurrent intent and slot label context 94.”
Data Augmentation for Spoken Language Understanding via Joint Variational Generation (2018, background): “In recent years, joint language understanding has become a popular approach, as studies show a synergetic effect of jointly training slot ﬁlling and intent identiﬁcation (Guo et al. 2014; Chen et al. 2016).”
BERT for Joint Intent Classification and Slot Filling (2019, methodology): “Joint modeling approaches include CNNCRF (Xu and Sarikaya, 2013), RecNN (Guo et al., 2014), joint RNN-LSTM (Hakkani-Tür et al.”
Adversarial Adaptation of Synthetic or Stale Data (2017, background): “…of prior works on enhancing model capability and features: jointly modeling intent and slot predictions (Jeong and Lee, 2008; Xu and Sarikaya, 2013; Guo et al., 2014; Zhang and Wang, 2016; Liu and Lane, 2016a,b), modeling SLU models with web search click logs (Li et al., 2009; Kim et al., 2015a)…”