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Spoken and Multimodal Dialog Systems and Applications (SLP-SMMD)

Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager


Despite many recent advances for the design of dialogue systems, a true bottleneck remains the acquisition of data required to train its components. Unlike many other language processing applications, dialogue systems require interactions with users, therefore it is complex to develop them with pre-recorded data. Building on previous works, on-line learning is pursued here as a most convenient way to address the issue. Data collection, annotation and use in learning algorithms are performed in a single process.

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Authors:
Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre
Submitted On:
10 May 2019 - 5:56pm
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Poster_ICASSP_RIou_Jabaian_Huet_Lefevre.pdf

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[1] Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre, "Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4416. Accessed: Aug. 19, 2019.
@article{4416-19,
url = {http://sigport.org/4416},
author = {Matthieu Riou; Bassam Jabaian; Stéphane Huet; Fabrice Lefèvre },
publisher = {IEEE SigPort},
title = {Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager},
year = {2019} }
TY - EJOUR
T1 - Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager
AU - Matthieu Riou; Bassam Jabaian; Stéphane Huet; Fabrice Lefèvre
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4416
ER -
Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre. (2019). Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager. IEEE SigPort. http://sigport.org/4416
Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre, 2019. Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager. Available at: http://sigport.org/4416.
Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre. (2019). "Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager." Web.
1. Matthieu Riou, Bassam Jabaian, Stéphane Huet, Fabrice Lefèvre. Joint On-line Learning of a Zero-Shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4416

Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation


Off-the-shelf speech recognizers are error-prone in specialized domains; we aim to mitigate the impact of these errors for downstream classification tasks without in-domain speech training data, by augmenting available typewritten text training data with inferred phonetic information. We apply our method to mitigate the effects of the lack of speech training data when converting a typed chatbot to a spoken language interface.

Paper available here: https://ieeexplore.ieee.org/document/8682550

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Authors:
Adam Stiff, Prashant Serai, Eric Fosler-Lussier
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9 May 2019 - 3:50pm
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Conference poster

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[1] Adam Stiff, Prashant Serai, Eric Fosler-Lussier, "Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4127. Accessed: Aug. 19, 2019.
@article{4127-19,
url = {http://sigport.org/4127},
author = {Adam Stiff; Prashant Serai; Eric Fosler-Lussier },
publisher = {IEEE SigPort},
title = {Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation},
year = {2019} }
TY - EJOUR
T1 - Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation
AU - Adam Stiff; Prashant Serai; Eric Fosler-Lussier
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4127
ER -
Adam Stiff, Prashant Serai, Eric Fosler-Lussier. (2019). Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation. IEEE SigPort. http://sigport.org/4127
Adam Stiff, Prashant Serai, Eric Fosler-Lussier, 2019. Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation. Available at: http://sigport.org/4127.
Adam Stiff, Prashant Serai, Eric Fosler-Lussier. (2019). "Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation." Web.
1. Adam Stiff, Prashant Serai, Eric Fosler-Lussier. Improving Human-Computer Interaction in Low-Resource Settings with Text-to-Phonetic Data Augmentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4127

SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION

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Authors:
Qian Chen, Wen Wang
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7 May 2019 - 10:12pm
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Poster of ICASSP2019

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[1] Qian Chen, Wen Wang, "SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3991. Accessed: Aug. 19, 2019.
@article{3991-19,
url = {http://sigport.org/3991},
author = {Qian Chen; Wen Wang },
publisher = {IEEE SigPort},
title = {SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION},
year = {2019} }
TY - EJOUR
T1 - SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION
AU - Qian Chen; Wen Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3991
ER -
Qian Chen, Wen Wang. (2019). SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION. IEEE SigPort. http://sigport.org/3991
Qian Chen, Wen Wang, 2019. SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION. Available at: http://sigport.org/3991.
Qian Chen, Wen Wang. (2019). "SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION." Web.
1. Qian Chen, Wen Wang. SEQUENTIAL MATCHING MODEL FOR END-TO-END MULTI-TURN RESPONSE SELECTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3991

Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'

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Authors:
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li
Submitted On:
13 April 2019 - 1:34am
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Poster-DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS.pdf

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[1] Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3890. Accessed: Aug. 19, 2019.
@article{3890-19,
url = {http://sigport.org/3890},
author = {Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li },
publisher = {IEEE SigPort},
title = {Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'},
year = {2019} }
TY - EJOUR
T1 - Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'
AU - Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3890
ER -
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. IEEE SigPort. http://sigport.org/3890
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, 2019. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. Available at: http://sigport.org/3890.
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'." Web.
1. Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS' [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3890

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning


This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts.

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Authors:
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
Submitted On:
22 April 2018 - 12:00pm
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Poster for Advantage A2C Dialogue Policy Learning

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[1] Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3134. Accessed: Aug. 19, 2019.
@article{3134-18,
url = {http://sigport.org/3134},
author = {Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong },
publisher = {IEEE SigPort},
title = {Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning},
year = {2018} }
TY - EJOUR
T1 - Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
AU - Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3134
ER -
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. IEEE SigPort. http://sigport.org/3134
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, 2018. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. Available at: http://sigport.org/3134.
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning." Web.
1. Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3134

Attention-based Dialog State Tracking for Conversational Interview Coaching


This study proposes an approach to dialog state tracking (DST) in a conversational interview coaching system. For the interview coaching task, the semantic slots, used mostly in traditional dialog systems, are difficult to define manually. This study adopts the topic profile of the response from the interviewee as the dialog state representation. In addition, as the response generally consists of several sentences, the summary vector obtained from a long short-term memory neural network (LSTM) is likely to contain noisy information from many irrelevant sentences.

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Authors:
Kun-Yi Huang, Chu-Kwang Chen
Submitted On:
12 April 2018 - 11:49pm
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ICASSP2018_Poster_20180410-3_Wu.pdf

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[1] Kun-Yi Huang, Chu-Kwang Chen, "Attention-based Dialog State Tracking for Conversational Interview Coaching", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2576. Accessed: Aug. 19, 2019.
@article{2576-18,
url = {http://sigport.org/2576},
author = {Kun-Yi Huang; Chu-Kwang Chen },
publisher = {IEEE SigPort},
title = {Attention-based Dialog State Tracking for Conversational Interview Coaching},
year = {2018} }
TY - EJOUR
T1 - Attention-based Dialog State Tracking for Conversational Interview Coaching
AU - Kun-Yi Huang; Chu-Kwang Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2576
ER -
Kun-Yi Huang, Chu-Kwang Chen. (2018). Attention-based Dialog State Tracking for Conversational Interview Coaching. IEEE SigPort. http://sigport.org/2576
Kun-Yi Huang, Chu-Kwang Chen, 2018. Attention-based Dialog State Tracking for Conversational Interview Coaching. Available at: http://sigport.org/2576.
Kun-Yi Huang, Chu-Kwang Chen. (2018). "Attention-based Dialog State Tracking for Conversational Interview Coaching." Web.
1. Kun-Yi Huang, Chu-Kwang Chen. Attention-based Dialog State Tracking for Conversational Interview Coaching [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2576

Attention-based Dialog State Tracking for Conversational Interview Coaching


This study proposes an approach to dialog state tracking (DST) in a conversational interview coaching system. For the interview coaching task, the semantic slots, used mostly in traditional dialog systems, are difficult to define manually. This study adopts the topic profile of the response from the interviewee as the dialog state representation. In addition, as the response generally consists of several sentences, the summary vector obtained from a long short-term memory neural network (LSTM) is likely to contain noisy information from many irrelevant sentences.

Paper Details

Authors:
Kun-Yi Huang, Chu-Kwang Chen
Submitted On:
12 April 2018 - 11:50pm
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ICASSP2018_Poster_20180410-3_Wu.pdf

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[1] Kun-Yi Huang, Chu-Kwang Chen, "Attention-based Dialog State Tracking for Conversational Interview Coaching", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2531. Accessed: Aug. 19, 2019.
@article{2531-18,
url = {http://sigport.org/2531},
author = {Kun-Yi Huang; Chu-Kwang Chen },
publisher = {IEEE SigPort},
title = {Attention-based Dialog State Tracking for Conversational Interview Coaching},
year = {2018} }
TY - EJOUR
T1 - Attention-based Dialog State Tracking for Conversational Interview Coaching
AU - Kun-Yi Huang; Chu-Kwang Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2531
ER -
Kun-Yi Huang, Chu-Kwang Chen. (2018). Attention-based Dialog State Tracking for Conversational Interview Coaching. IEEE SigPort. http://sigport.org/2531
Kun-Yi Huang, Chu-Kwang Chen, 2018. Attention-based Dialog State Tracking for Conversational Interview Coaching. Available at: http://sigport.org/2531.
Kun-Yi Huang, Chu-Kwang Chen. (2018). "Attention-based Dialog State Tracking for Conversational Interview Coaching." Web.
1. Kun-Yi Huang, Chu-Kwang Chen. Attention-based Dialog State Tracking for Conversational Interview Coaching [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2531

CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE


In this work, we investigate mapping both natural language food and quantity descriptions to matching USDA database entries. We demonstrate that a convolutional neural network (CNN) model with a softmax layer on top to directly predict the most likely database matches outperforms our previous state-of-the-art approach of learning binary classification and subsequently ranking database entries using similarity scores with the learned embeddings.

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Authors:
Mandy Korpusik, James Glass
Submitted On:
12 April 2018 - 12:21pm
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icassp_2018.pdf

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[1] Mandy Korpusik, James Glass, "CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2431. Accessed: Aug. 19, 2019.
@article{2431-18,
url = {http://sigport.org/2431},
author = {Mandy Korpusik; James Glass },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE},
year = {2018} }
TY - EJOUR
T1 - CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE
AU - Mandy Korpusik; James Glass
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2431
ER -
Mandy Korpusik, James Glass. (2018). CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE. IEEE SigPort. http://sigport.org/2431
Mandy Korpusik, James Glass, 2018. CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE. Available at: http://sigport.org/2431.
Mandy Korpusik, James Glass. (2018). "CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE." Web.
1. Mandy Korpusik, James Glass. CONVOLUTIONAL NEURAL NETWORKS AND MULTITASK STRATEGIES FOR SEMANTIC MAPPING OF NATURAL LANGUAGE INPUT TO A STRUCTURED DATABASE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2431

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager


Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the

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Authors:
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng
Submitted On:
10 March 2017 - 2:14pm
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E2E_ICASSP17.pdf

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[1] Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng, "End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1736. Accessed: Aug. 19, 2019.
@article{1736-17,
url = {http://sigport.org/1736},
author = {Xuesong Yang; Yun-Nung Chen; Dilek Hakkani-Tur; Paul Crook; Xiujun Li; Jianfeng Gao; Li Deng },
publisher = {IEEE SigPort},
title = {End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager},
year = {2017} }
TY - EJOUR
T1 - End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
AU - Xuesong Yang; Yun-Nung Chen; Dilek Hakkani-Tur; Paul Crook; Xiujun Li; Jianfeng Gao; Li Deng
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1736
ER -
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng. (2017). End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager. IEEE SigPort. http://sigport.org/1736
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng, 2017. End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager. Available at: http://sigport.org/1736.
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng. (2017). "End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager." Web.
1. Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng. End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1736

Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching


The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview.

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Authors:
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu
Submitted On:
22 November 2016 - 11:33pm
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MingHsiangSu-IALP 2016.pdf

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[1] Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu, "Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1301. Accessed: Aug. 19, 2019.
@article{1301-16,
url = {http://sigport.org/1301},
author = {Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; Kuan-Jung Lai and Chung-Hsien Wu },
publisher = {IEEE SigPort},
title = {Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching},
year = {2016} }
TY - EJOUR
T1 - Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching
AU - Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; Kuan-Jung Lai and Chung-Hsien Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1301
ER -
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. (2016). Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching. IEEE SigPort. http://sigport.org/1301
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu, 2016. Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching. Available at: http://sigport.org/1301.
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. (2016). "Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching." Web.
1. Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1301

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