[PDF] Top 20 Deep Neural Network Approach for the Dialog State Tracking Challenge
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Deep Neural Network Approach for the Dialog State Tracking Challenge
... The domain of the DSTC is bus route informa- tion in the city of Pittsburgh, but the presented technique is easily transferable to new domains, with the learned models in fact being domain in- dependent. No domain ... See full document
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Word Based Dialog State Tracking with Recurrent Neural Networks
... the state of a spoken dialog have been shown to outperform traditional generative ...based tracking method which maps di- rectly from the speech recognition results to the dialog state ... See full document
8
The SJTU System for Dialog State Tracking Challenge 2
... Dialog state tracking challenge provides a common testbed for state tracking al- ...Dialogue State Tracking Challenge in ...a deep neural ... See full document
9
A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information
... a deep neural network method from Team 1), which are opti- mised specifically for ...our approach becomes much less competi- tive when evaluated based on the ROC curve met- rics, as ... See full document
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Dialog state tracking, a machine reading approach using Memory Network
... human-to-human dialog sys- tems, dialog-level annotations remains a common practice of annotation especially in personal assis- tance, customer care dialogs and, in a more gen- eral sense, industrial ... See full document
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Task Lineages: Dialog State Tracking for Flexible Interaction
... DST approach that depart from conventional dia- log state tracking ...the dialog state (hence losing past states) TL- DST adopts a dynamically growing structure, pro- viding a richer ... See full document
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Recipe For Building Robust Spoken Dialog State Trackers: Dialog State Tracking Challenge System Description
... both state tracking and decision making, most earlier studies adopted generative temporal models, the typical way to formulate belief state updates for POMDP-based systems (Williams and Young, ... See full document
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Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
... and Jurc´ıcek (2015). We chose this architecture over other successful DST approaches that oper- ate on the turn-level of the dialogs (Henderson et al., 2014c; Mrksic et al., 2017) because it pro- cesses the system and ... See full document
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Comparative Error Analysis of Dialog State Tracking
... the challenge is to carefully exam- ine the impact of different approaches to dialog state ...the challenge, there were quite a variety of approaches taken (though not all teams provided a ... See full document
10
Dialog State Tracking: A Neural Reading Comprehension Approach
... hence state-of-the-art models are developed in such a way that a fixed vocabulary for an answer is usu- ally not ...previous dialog state tracking methods and the re- cent advances in reading ... See full document
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Web style ranking and SLU combination for dialog state tracking
... for dialog state tracking was to consider a small, fixed number of states and then apply a multinomial classifier (Bohus and Rudnicky, ...discriminative approach is to score each state ... See full document
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Markovian Discriminative Modeling for Dialog State Tracking
... A dialog can be naturally seen as a temporal se- quence involving a user and an agent, where strong dependencies exist between adjacent ...a dialog session, the agent’s perception of it will tend to evolve ... See full document
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Discriminative state tracking for spoken dialog systems
... the dialog state tracker is not predicting the contents of the dialog state hypotheses; the di- alog state hypotheses contents are given by some external process, and the task is to ... See full document
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Structured Discriminative Model For Dialog State Tracking
... the Dialog State Tracking Challenge 3 (Lee and Eskenazi, ...that dialog state tracking methods in general are effective in improving robustness to ASR ... See full document
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Multi domain learning and generalization in dialog state tracking
... in dialog state ...the Dialog State Tracking Challenge, generalization has been studied through varying levels of mis-match between training and test ... See full document
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VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System
... In this paper, we propose a Vehicle Passenger Detection Sys- tem (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional ... See full document
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Phonocardiographic sensing using deep learning for abnormal heartbeat detection
... proposed approach achieved the best result on sensitivity, which ensures that it correctly identifies patients with abnormality, minimising the false ...proposed approach achieves the best results on ... See full document
8
Human corneal state prediction from topographical maps using a deep neural network and a support vector machine
... uses deep act features from the four refractive maps provided by the Pentacam®, and then enters these features into the support vector machine (SVM) classifier for accurate diagnosing that supports the clinician’s ... See full document
7
Deep Learning Based Feature Silencing for Accurate Concrete Crack Detection
... proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete ...proposed network alleviates the effect of gradient vanishing problem ... See full document
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Dialog State Tracking using Conditional Random Fields
... dialog state. The graphical model is illustrated in figure 1. To predict dialog state at turn t, the N -best items from turn 1 to t are all ...the dialog state. Compared to the ... See full document
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