spoken language understanding
Spoken Language Understanding for Personal Computers
... SPOKEN LANGUAGE UNDERSTANDING FOR PERSONAL COMPUTERS S P O K E N L A N G U A G E U N D E R S T A N D I N G FOR P E R S O N A L C O M P U T E R S George M White David Nagel Apple Computer Inc 20525 Mar[.] ...
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Neural Lexicons for Slot Tagging in Spoken Language Understanding
... There have been many previous studies involving spoken language understanding for the slot tag- ging problem. Yao et al. (2014) investigate the use of LSTMs for slot tagging and compare the ...
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Sequential Dialogue Context Modeling for Spoken Language Understanding
... the spoken language understanding module, which typically parses user utterances into semantic frames, composed of do- mains, intents and slots (Tur and De Mori, 2011), that can then be processed by ...
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Exploiting multiple hypotheses for Multilingual Spoken Language Understanding
... Spoken Language Understanding (SLU) is one of the key modules in many voice-driven human- computer interaction ...most Spoken Dialog Systems since the semantic information to be ex- tracted is ...
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Structured Learning for Context aware Spoken Language Understanding of Robotic Commands
... for Spoken Language Understand- ing that force the above research perspective: this is obtained by extending the linguistic evidence that can be extracted from the uttered commands with perceptual evidence ...
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Data Augmentation with Atomic Templates for Spoken Language Understanding
... Spoken Language Understanding (SLU) con- verts user utterances into structured seman- tic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, ...
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Data Augmentation for Spoken Language Understanding via Joint Variational Generation
... Spoken Language Understanding The SLU task is one of more mature research areas in ...Joint language understanding models that jointly predict slot labels and intents gained significant ...
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A Weakly Supervised Learning Approach for Spoken Language Understanding
... for spoken lan- guage understanding in domain-specific dialogue ...of spoken language understanding as a suc- cessive classification ...the understanding robustness and deepness ...
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Multi Site Data Collection and Evaluation in Spoken Language Understanding
... Multi Site Data Collection and Evaluation in Spoken Language Understanding M u l t i S i t e D a t a C o l l e c t i o n a n d E v a l u a t i o n in S p o k e n L a n g u a g e U n d e r s t a n d i[.] ...
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Active Learning in Noisy Conditions for Spoken Language Understanding
... Spoken language understanding (SLU) is currently an emerging field in the intersection of speech processing and natural language processing (Tur and De Mori, ...natural language ...
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Corpus Development Activities at the Center for Spoken Language Understanding
... Corpus Development Activities at the Center for Spoken Language Understanding Corpus D e v e l o p m e n t Activities at the Center for Spoken Language U n d e r s t a n d i n g Ron Cole, Mike Noel, D[.] ...
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Exploiting Non Local Features for Spoken Language Understanding
... a language understanding task, the head word dependencies or parse tree path are successfully applied to learn and predict semantic roles, especially those with ambiguous labels (Gildea and Jurafsky, ...a ...
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A Self Attentive Model with Gate Mechanism for Spoken Language Understanding
... Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue ...
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CM Net: A Novel Collaborative Memory Network for Spoken Language Understanding
... Memory Network Memory network is a gen- eral machine learning framework introduced by Weston et al. (2014), which have been shown effective in question answering (Weston et al., 2014; Sukhbaatar et al., 2015), machine ...
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Integrating Syntax and Semantics into Spoken Language Understanding
... Integrating Syntax and Semantics into Spoken Language Understanding I n t e g r a t i n g S y n t a x a n d S e m a n t i c s i n t o S p o k e n L a n g u a g e U n d e r s t a n d i n g 1 Lynette Hi[.] ...
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Nonparametric Bayesian Models for Spoken Language Understanding
... In this paper, we propose a new generative ap- proach for semantic slot filling task in spoken language understanding using a nonparamet- ric Bayesian formalism. Slot filling is typi- cally ...
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Re Ranking Models for Spoken Language Understanding
... Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: generative ...
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Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
... We present a generative neural network model for slot filling based on a sequence- to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in ...
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Practical Semantic Parsing for Spoken Language Understanding
... natural language utterances into logi- cal forms that can be directly used as queries to get a ...for Spoken Language Understanding ...
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Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
... Spoken language understanding (SLU) is a key technique in today’s conversational systems such as Apple Siri, Amazon Alexa, and Microsoft Cor- ...
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