Spoken language understanding using long short-term memory neural networks

By Kaisheng Yao, Baolin Peng, Yu Zhang, Dong Yu, Geoffry Zweig, Yangyang Shi. 2014

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Speech & Audio

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Citations in the corpus

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems (2015, background): “It has been shown to be effective in a variety of tasks, such as speech recognition (Graves et al., 2013b), handwriting recognition (Graves et al., 2009), spoken language understanding (Yao et al., 2014), and machine translation (Sutskever et al., 2014).”
A Network-based End-to-End Trainable Task-oriented Dialogue System (2016, background): “Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 438–449, Valencia, Spain, April 3-7, 2017. c©2017 Association for Computational Linguistics”
A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, methodology): “They are designed to either train a slot ﬁlling model independently (Yao et al., 2014; Peng et al., 2015; Kurata et al., 2016; Hakkani-T¨ur et al., 2016; Liu and Lane, 2016; Deng et al., 2019; Ray et al., 2019) or jointly with the other intent detection task in SLU (Guo et al., 2014; Liu and Lane,…”
Sequence-to-sequence neural net models for grapheme-to-phoneme conversion (2015, methodology): “As in [5], we use an LSTM [17] as the basic recurrent network unit because it has shown better performance than simple RNNs on language understanding [24] and acoustic modeling [25] tasks.”
Bidirectional LSTM-CRF Models for Sequence Tagging (2015, background): “In speech language understanding community, recurrent neural network (Mesnil et al., 2013; Yao et al., 2014) and convolutional nets (Xu and Sarikaya, 2013) based models have been recently proposed.”
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling (2016, background): “Popular approaches to solving sequence labeling problems include maximum entropy Markov models (MEMMs) [3], conditional random fields (CRFs) [4], and recurrent neural networks (RNNs) [5, 6, 7].”
Neural Belief Tracker: Data-Driven Dialogue State Tracking (2016, methodology): “This hinders scaling to larger, more realistic domains.”
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (2018, methodology): “…random ﬁelds (CRF) (Ray-1 The code is available at: https://github.com/ MiuLab/SlotGated-SLU . mond and Riccardi, 2007) and recurrent neural network (RNN) (Yao et al., 2014), and different classiﬁcation methods, such as support vector machine (SVM) and RNN, have been applied to intent prediction.”
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (2018, methodology): “The most effective algorithms are based on the structures of sequence to sequence models (or ”encoder-decoder” models), and generate the intents and semantic tags either using separate models((Yao et al., 2014; Mesnil et al., 2015; Peng and Yao, 2015; Kurata et al., 2016; Hahn et al., 2011)) or a jo”
A bi-directional LSTM approach for polyphone disambiguation in Mandarin Chinese (2016, methodology): “RNNs, especially those networks with LSTM cells, have recently produced promising results on a variety of tasks including language modeling [19] [20], speech recognition [21] and other sequential labelling tasks [14, 15, 16, 17, 22, 23, 24].”
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning (2017, methodology): “Our composite task-completion dialogue agent consists of four components: (1) an LSTM-based language understanding module (Hakkani-T¨ur et al., 2016; Yao et al., 2014) for identifying user intents and extracting associated slots; (2) a state tracker for tracking the dialogue state; (3) a dialogue…”
End-to-End Reinforcement Learning of Dialogue Agents for Information Access (2016, unknown): “I think it came out in 1993.”
User Information Augmented Semantic Frame Parsing Using Progressive Neural Networks (2018, methodology): “Existing approaches includes the independent models for learning intent detection [1, 2] and slot filling [3, 4, 5, 6, 7, 8] separately as well as joint models to learn these two tasks together [9, 10, 11, 2].”
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (2019, background): “Slot ﬁlling can be treated as a sequence labeling task, and the popular approaches are conditional random ﬁelds (CRF) (Raymond and Ric-cardi, 2007) and recurrent neural networks (RNN) (Xu and Sarikaya, 2013; Yao et al., 2014).”
Translation Quality Estimation using Recurrent Neural Network (2016, methodology): “Different researchers use slightly different LSTM variants (Graves, 2013; Yao et al., 2014; Jozefowicz et al., 2015).”
Data Augmentation for Spoken Language Understanding via Joint Variational Generation (2018, methodology): “Plain RNNs and LSTMs were ﬁrst explored in (Mesnil et al. 2015; Yao et al. 2014).”
Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM (2016, background): “Especially with the re-discovery of LSTM cells [25] for RNNs, this architecture has started to emerge [26].”