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Spoken Language Understanding (SLP-UNDE)

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
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20 April 2018 - 12:23pm
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poster_icassp2018_v2.pptx

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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/3110. Accessed: Jul. 17, 2018.
@article{3110-18,
url = {http://sigport.org/3110},
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/3110
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/3110
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/3110.
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/3110

AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION


Social signals such as laughter and fillers are often observed in natural conversation, and they play various roles in human-to-human communication. Detecting these events is useful for transcription systems to generate rich transcription and for dialogue systems to behave as we do such as synchronized laughing or attentive listening. We have studied an end-to-end approach to directly detect social signals from speech by using connectionist temporal classification (CTC), which is one of the end-to-end sequence labelling models.

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Authors:
Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara
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17 April 2018 - 7:49pm
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201804_ICASSP2018_poster.pdf

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[1] Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara, "AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2947. Accessed: Jul. 17, 2018.
@article{2947-18,
url = {http://sigport.org/2947},
author = {Hirofumi Inaguma; Masato Mimura; Koji Inoue; Kazuyoshi Yoshii; Tatsuya Kawahara },
publisher = {IEEE SigPort},
title = {AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION
AU - Hirofumi Inaguma; Masato Mimura; Koji Inoue; Kazuyoshi Yoshii; Tatsuya Kawahara
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2947
ER -
Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara. (2018). AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/2947
Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara, 2018. AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION. Available at: http://sigport.org/2947.
Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara. (2018). "AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION." Web.
1. Hirofumi Inaguma, Masato Mimura, Koji Inoue, Kazuyoshi Yoshii, Tatsuya Kawahara. AN END-TO-END APPROACH TO JOINT SOCIAL SIGNAL DETECTION AND AUTOMATIC SPEECH RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2947

Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling

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19 April 2018 - 3:10pm
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schumann_icassp_presentation.pdf

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schumann_icassp_presentation.pdf

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[1] , "Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2903. Accessed: Jul. 17, 2018.
@article{2903-18,
url = {http://sigport.org/2903},
author = { },
publisher = {IEEE SigPort},
title = {Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling},
year = {2018} }
TY - EJOUR
T1 - Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2903
ER -
. (2018). Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling. IEEE SigPort. http://sigport.org/2903
, 2018. Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling. Available at: http://sigport.org/2903.
. (2018). "Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling." Web.
1. . Incorporating ASR Errors with Attention-based, Jointly Trained RNN for Intent Detection and Slot Filling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2903

ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION

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Authors:
Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung
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14 April 2018 - 8:45am
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attention-based-lstm-poster.pdf

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[1] Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung, "ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2829. Accessed: Jul. 17, 2018.
@article{2829-18,
url = {http://sigport.org/2829},
author = {Genta Indra Winata; Onno Pepijn Kampman; Pascale Fung },
publisher = {IEEE SigPort},
title = {ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION},
year = {2018} }
TY - EJOUR
T1 - ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION
AU - Genta Indra Winata; Onno Pepijn Kampman; Pascale Fung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2829
ER -
Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung. (2018). ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION. IEEE SigPort. http://sigport.org/2829
Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung, 2018. ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION. Available at: http://sigport.org/2829.
Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung. (2018). "ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION." Web.
1. Genta Indra Winata, Onno Pepijn Kampman, Pascale Fung. ATTENTION-BASED LSTM FOR PSYCHOLOGICAL STRESS DETECTION FROM SPOKEN LANGUAGE USING DISTANT SUPERVISION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2829

Lexico-acoustic Neural-based Models for Dialog Act Classification


Recent works have proposed neural models for dialog act classification in spoken dialogs.
However, they have not explored the role and the usefulness of acoustic information.
We propose a neural model that processes both lexical and acoustic features for classification.
Our results on two benchmark datasets reveal that acoustic features are helpful in improving the overall accuracy.

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Authors:
Daniel Ortega, Ngoc Thang Vu
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14 April 2018 - 12:45am
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icassp-2018-poster.pdf

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[1] Daniel Ortega, Ngoc Thang Vu, "Lexico-acoustic Neural-based Models for Dialog Act Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2796. Accessed: Jul. 17, 2018.
@article{2796-18,
url = {http://sigport.org/2796},
author = {Daniel Ortega; Ngoc Thang Vu },
publisher = {IEEE SigPort},
title = {Lexico-acoustic Neural-based Models for Dialog Act Classification},
year = {2018} }
TY - EJOUR
T1 - Lexico-acoustic Neural-based Models for Dialog Act Classification
AU - Daniel Ortega; Ngoc Thang Vu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2796
ER -
Daniel Ortega, Ngoc Thang Vu. (2018). Lexico-acoustic Neural-based Models for Dialog Act Classification. IEEE SigPort. http://sigport.org/2796
Daniel Ortega, Ngoc Thang Vu, 2018. Lexico-acoustic Neural-based Models for Dialog Act Classification. Available at: http://sigport.org/2796.
Daniel Ortega, Ngoc Thang Vu. (2018). "Lexico-acoustic Neural-based Models for Dialog Act Classification." Web.
1. Daniel Ortega, Ngoc Thang Vu. Lexico-acoustic Neural-based Models for Dialog Act Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2796

Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification


We present an end-to-end multi-scale Convolutional Neural
Network (CNN) framework for topic identification (topic ID).
In this work, we examined multi-scale CNN for classification
using raw text input. Topical word embeddings are learnt at
multiple scales using parallel convolutional layers. A technique
to integrate verification and identification objectives is
examined to improve topic ID performance. With this approach,
we achieved significant improvement in identification
task. We evaluated our framework on two contrasting

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Authors:
Raghavendra Pappagari, Jesus Villalba, Najim Dehak
Submitted On:
13 April 2018 - 4:16pm
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[1] Raghavendra Pappagari, Jesus Villalba, Najim Dehak, "Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2758. Accessed: Jul. 17, 2018.
@article{2758-18,
url = {http://sigport.org/2758},
author = {Raghavendra Pappagari; Jesus Villalba; Najim Dehak },
publisher = {IEEE SigPort},
title = {Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification},
year = {2018} }
TY - EJOUR
T1 - Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification
AU - Raghavendra Pappagari; Jesus Villalba; Najim Dehak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2758
ER -
Raghavendra Pappagari, Jesus Villalba, Najim Dehak. (2018). Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification. IEEE SigPort. http://sigport.org/2758
Raghavendra Pappagari, Jesus Villalba, Najim Dehak, 2018. Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification. Available at: http://sigport.org/2758.
Raghavendra Pappagari, Jesus Villalba, Najim Dehak. (2018). "Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification." Web.
1. Raghavendra Pappagari, Jesus Villalba, Najim Dehak. Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2758

A Joint Multi-Task Learning Framework For Spoken Language Understanding

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Authors:
Cunliang Kong,Yan Zhao
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13 April 2018 - 5:34am
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A Joint Multi-Task Learning Framework For Spoken Language.pdf

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[1] Cunliang Kong,Yan Zhao, "A Joint Multi-Task Learning Framework For Spoken Language Understanding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2669. Accessed: Jul. 17, 2018.
@article{2669-18,
url = {http://sigport.org/2669},
author = {Cunliang Kong;Yan Zhao },
publisher = {IEEE SigPort},
title = {A Joint Multi-Task Learning Framework For Spoken Language Understanding},
year = {2018} }
TY - EJOUR
T1 - A Joint Multi-Task Learning Framework For Spoken Language Understanding
AU - Cunliang Kong;Yan Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2669
ER -
Cunliang Kong,Yan Zhao. (2018). A Joint Multi-Task Learning Framework For Spoken Language Understanding. IEEE SigPort. http://sigport.org/2669
Cunliang Kong,Yan Zhao, 2018. A Joint Multi-Task Learning Framework For Spoken Language Understanding. Available at: http://sigport.org/2669.
Cunliang Kong,Yan Zhao. (2018). "A Joint Multi-Task Learning Framework For Spoken Language Understanding." Web.
1. Cunliang Kong,Yan Zhao. A Joint Multi-Task Learning Framework For Spoken Language Understanding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2669

PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS


In recent years, neural networks (NN) have achieved remarkable performance improvement in text classification due to
their powerful ability to encode discriminative features by incorporating label information into model training. Inspired
by the success of NN in text classification, we propose a pseudo-supervised neural network approach for text clustering.
The neural network is trained in a supervised fashion with pseudo-labels, which are provided by the cluster labels

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Authors:
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling
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13 April 2018 - 4:05am
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ICASSP-Chen Peixin_v2.pdf

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[1] Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2647. Accessed: Jul. 17, 2018.
@article{2647-18,
url = {http://sigport.org/2647},
author = {Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling },
publisher = {IEEE SigPort},
title = {PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS
AU - Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2647
ER -
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. IEEE SigPort. http://sigport.org/2647
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, 2018. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. Available at: http://sigport.org/2647.
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS." Web.
1. Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2647

Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications


We propose a semi-supervised learning method to improve classification performance in scenarios with limited labeled
data. We employ adaptation strategies such as entropy-filtering and self-training, and show that our method achieves

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Authors:
Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan
Submitted On:
12 April 2018 - 6:08pm
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[1] Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan, "Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2507. Accessed: Jul. 17, 2018.
@article{2507-18,
url = {http://sigport.org/2507},
author = {Pavlos Papadopoulos; Ruchir Travadi; Daniel Bone; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications},
year = {2018} }
TY - EJOUR
T1 - Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications
AU - Pavlos Papadopoulos; Ruchir Travadi; Daniel Bone; Shrikanth Narayanan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2507
ER -
Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan. (2018). Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications. IEEE SigPort. http://sigport.org/2507
Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan, 2018. Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications. Available at: http://sigport.org/2507.
Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan. (2018). "Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications." Web.
1. Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan. Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2507

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: Jul. 17, 2018.
@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

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