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Intent classification is a major task in spoken language understanding (SLU).
BERT for joint intent classification and slot filling
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Roberta: A robustly optimized BERT pretraining approach
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Huggingface’s transformers: State-of-the-art natural language processing
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The atis spoken language systems pilot corpus
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Spoken language understanding
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Joint semantic utterance classification and slot filling with recursive neural networks
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Detecting out-of-domain utterances addressed to a virtual personal assistant
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A comparative study of neural network models for lexical intent classification
Suman Ravuri and Andreas Stoicke. 2015 · 2015
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Intent detection using semantically enriched word embeddings
Joo-Kyung Kim, Gokhan Tur, Asli Celikyilmaz, Bin Cao, and Ye-Yi Wang. 2016 · 2016
Cited alongside, same era.
Attention-based recurrent neural network models for joint intent detection and slot filling
Bing Liu and Ian Lane. 2016 · 2016
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A joint model of intent determination and slot filling for spoken language understanding
Xiaodong Zhang and Houfeng Wang. 2016 · 2016
Cited alongside, same era.
Utterance intent classification of a spoken dialogue system with efficiently untied recursive autoencoders
Tsuneo Kato, Atsushi Nagai, Naoki Noda, Ryosuke Sumitomo, Jianming Wu, and Seiichi Yamamoto. 2017 · 2017
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ONENET: joint domain, intent, slot prediction for spoken language understanding
Young-Bum Kim, Sungjin Lee, and Karl Stratos. 2017 · 2017
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018 · 2018
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Learning out-of-vocabulary words in intelligent personal agents
Avik Ray, Yilin Shen, and Hongxia Jin. 2018 · 2018
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Out-of-domain detection based on generative adversarial network
Seonghan Ryu, Sangjun Koo, Hwanjo Yu, and Gary Geunbae Lee. 2018 · 2018
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A bi-model based rnn semantic frame parsing model for intent detection and slot filling
Yu Wang, Yilin Shen, and Hongxia Jin. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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An evaluation dataset for intent classification and out-of-scope prediction
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Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018 · 2018
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Slot-gated modeling for joint slot filling and intent prediction
Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, and Yun-Nung Chen. 2018 · 2018
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Dan Hendrycks, Mantas Mazeika, and Thomas G Dietterich. 2018 · 2018
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SkillBot: Towards automatic skill development via user demonstration
Yilin Shen, Avik Ray, Hongxia Jin, and Sandeep Nama. 2019b
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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason mars. 2019 · 2019
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Fast domain adaptation of semantic parsers via paraphrase attention
Avik Ray, Yilin Shen, and Hongxia Jin. 2019 · 2019
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Out-of-domain detection for low-resource text classification tasks
Ming Tan, Yang Yu, Haoyu Wang, Dakuo Wang, Saloni Potdar, Shiyu Chang, and Mo Yu. 2019 · 2019
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Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira. 2020 · 2020
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Out-of-domain detection for natural language understanding in dialog systems
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