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

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

Xishuo Li

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Authors:
Xishuo Li
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: Apr. 20, 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

Baolin Peng
Yun-Nung Chen

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
Yun-Nung Chen
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
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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: Apr. 20, 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

Ming-Hsiang Su
Chung-Hsien Wu

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:
Ming-Hsiang Su
Chung-Hsien Wu
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: Apr. 20, 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

Ming-Hsiang Su
Chung-Hsien Wu

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:
Ming-Hsiang Su
Chung-Hsien Wu
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: Apr. 20, 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

Mandy Korpusik

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
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: Apr. 20, 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

Yun-Nung Chen

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

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Authors:
Yun-Nung Chen
Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng
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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: Apr. 20, 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

Chung-Hsien Wu

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.

Paper Details

Authors:
Chung-Hsien Wu
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: Apr. 20, 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

Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study

YING ZHOU

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Authors:
YING ZHOU
Ying Zhou, Fei Chen, Hui Chen,Nan Yan
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16 October 2016 - 1:06am
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Eyetracking PPT.ppt

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[1] Ying Zhou, Fei Chen, Hui Chen,Nan Yan, "Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1254. Accessed: Apr. 20, 2019.
@article{1254-16,
url = {http://sigport.org/1254},
author = {Ying Zhou; Fei Chen; Hui Chen;Nan Yan },
publisher = {IEEE SigPort},
title = {Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study},
year = {2016} }
TY - EJOUR
T1 - Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study
AU - Ying Zhou; Fei Chen; Hui Chen;Nan Yan
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1254
ER -
Ying Zhou, Fei Chen, Hui Chen,Nan Yan. (2016). Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study. IEEE SigPort. http://sigport.org/1254
Ying Zhou, Fei Chen, Hui Chen,Nan Yan, 2016. Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study. Available at: http://sigport.org/1254.
Ying Zhou, Fei Chen, Hui Chen,Nan Yan. (2016). "Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study." Web.
1. Ying Zhou, Fei Chen, Hui Chen,Nan Yan. Evaluation of a Multimodal 3-D Pronunciation Tutor for Learning Mandarin as a Second Language:An Eye-tracking Study [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1254

Realizing Speech to Gesture Conversion by Keyword Spotting

na zhao

The paper proposed a method to realize a speech-to-gesture conversion for communication between normal and speech-impaired people. Keyword spotting was employed to recognize the keywords from input speech signals. At the same time, the three dimensional gesture models of keywords were built by 3D modeling technology according to the "Chinese sign language". The speech-to-gesture conversion was finally realized by playing the corresponding 3D gestures with OpenGL from the results of keyword spotting.

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Authors:
na zhao
Na Zhao, Hongwu Yang
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14 October 2016 - 9:54pm
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keyword spotting, gesture modeling, speech to gesture conversion

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[1] Na Zhao, Hongwu Yang, "Realizing Speech to Gesture Conversion by Keyword Spotting", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1212. Accessed: Apr. 20, 2019.
@article{1212-16,
url = {http://sigport.org/1212},
author = {Na Zhao; Hongwu Yang },
publisher = {IEEE SigPort},
title = {Realizing Speech to Gesture Conversion by Keyword Spotting},
year = {2016} }
TY - EJOUR
T1 - Realizing Speech to Gesture Conversion by Keyword Spotting
AU - Na Zhao; Hongwu Yang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1212
ER -
Na Zhao, Hongwu Yang. (2016). Realizing Speech to Gesture Conversion by Keyword Spotting. IEEE SigPort. http://sigport.org/1212
Na Zhao, Hongwu Yang, 2016. Realizing Speech to Gesture Conversion by Keyword Spotting. Available at: http://sigport.org/1212.
Na Zhao, Hongwu Yang. (2016). "Realizing Speech to Gesture Conversion by Keyword Spotting." Web.
1. Na Zhao, Hongwu Yang. Realizing Speech to Gesture Conversion by Keyword Spotting [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1212

Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization

Yun-Nung Chen

Feature-Enrich Matrix Factorization for SLU at ICASSP16

Spoken language interfaces are being incorporated into various devices such as smart phones and TVs. However, dialogue systems may fail to respond correctly when users’ request functionality is not supported by currently installed apps. This paper proposes a feature-enriched matrix factorization (MF) approach to model open domain intents, which allows a system to dynamically add unexplored domains according to users’ requests.

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Authors:
Yun-Nung Chen
Ming Sun, Alexander I. Rudnicky, Anatole Gershman
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31 March 2016 - 7:51pm
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FeatureMF_poster.pdf

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[1] Ming Sun, Alexander I. Rudnicky, Anatole Gershman, "Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1079. Accessed: Apr. 20, 2019.
@article{1079-16,
url = {http://sigport.org/1079},
author = {Ming Sun; Alexander I. Rudnicky; Anatole Gershman },
publisher = {IEEE SigPort},
title = {Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization},
year = {2016} }
TY - EJOUR
T1 - Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization
AU - Ming Sun; Alexander I. Rudnicky; Anatole Gershman
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1079
ER -
Ming Sun, Alexander I. Rudnicky, Anatole Gershman. (2016). Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization. IEEE SigPort. http://sigport.org/1079
Ming Sun, Alexander I. Rudnicky, Anatole Gershman, 2016. Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization. Available at: http://sigport.org/1079.
Ming Sun, Alexander I. Rudnicky, Anatole Gershman. (2016). "Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization." Web.
1. Ming Sun, Alexander I. Rudnicky, Anatole Gershman. Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1079

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