Combining Impression Feature Representation for Multi-turn Conversational Question Answering

2020 ◽  
pp. 57-69
Author(s):  
Shaoling Jing ◽  
Shibo Hong ◽  
Dongyan Zhao ◽  
Haihua Xie ◽  
Zhi Tang
Keyword(s):  
Question Answering ◽  
Feature Representation

scholarly journals Visual Question Answering Through Adversarial Learning of Multi-modal Representation

2020 ◽  
Author(s):  
Iqbal Chowdhury ◽  
Kien Nguyen Thanh ◽  
Clinton fookes ◽  
Sridha Sridharan
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Feature Representation ◽  
Fusion Method ◽  
Adversarial Learning ◽  
Visual Question Answering ◽  
Proposed Model ◽  
Fusion Methods ◽  
Adversarial Training ◽  
Multimodal Representation

Solving the Visual Question Answering (VQA) task is a step towards achieving human-like reasoning capability of the machines. This paper proposes an approach to learn multimodal feature representation with adversarial training. The purpose of the adversarial training allows the model to learn from standard fusion methods in an unsupervised manner. The discriminator model is equipped with a siamese combinatin of two standard fusion method namely multimodal compact bilinear pooling and multimodal tucker fusion. Output multimodal feature representation from generator is a resultant of graph convolutional operation. The resultant multimodal representation of the adversarial training allows the proposed model to infer the correct answers from open-ended natural language questions from the VQA 2.0 dataset. An overall accuracy of 69.86\% demonstrates the accuracy of the proposed model.

scholarly journals Visual Question Answering Through Adversarial Learning of Multi-modal Representation

2020 ◽  
Author(s):  
Iqbal Chowdhury ◽  
Kien Nguyen Thanh ◽  
Clinton fookes ◽  
Sridha Sridharan
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Feature Representation ◽  
Fusion Method ◽  
Adversarial Learning ◽  
Visual Question Answering ◽  
Proposed Model ◽  
Fusion Methods ◽  
Adversarial Training ◽  
Multimodal Representation

Solving the Visual Question Answering (VQA) task is a step towards achieving human-like reasoning capability of the machines. This paper proposes an approach to learn multimodal feature representation with adversarial training. The purpose of the adversarial training allows the model to learn from standard fusion methods in an unsupervised manner. The discriminator model is equipped with a siamese combinatin of two standard fusion method namely multimodal compact bilinear pooling and multimodal tucker fusion. Output multimodal feature representation from generator is a resultant of graph convolutional operation. The resultant multimodal representation of the adversarial training allows the proposed model to infer the correct answers from open-ended natural language questions from the VQA 2.0 dataset. An overall accuracy of 69.86\% demonstrates the accuracy of the proposed model.

scholarly journals Medical Answer Selection Based on Two Attention Mechanisms with BiRNN

2018 ◽  
Vol 176 ◽  
pp. 01024 ◽  
Author(s):  
Jiajia Ma ◽  
Chao Che ◽  
Qiang Zhang
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Large Population ◽  
Relevant Information ◽  
Feature Representation ◽  
The Other ◽  
Learning Models ◽  
Medical Field ◽  
To Receive ◽  
Timely Treatment

The contradiction between the large population of China and the limited medical resources lead to the difficulty of getting medical services. The emergence of question answering (QA) system in the medical field allows people to receive timely treatment at home and alleviates the burden on hospitals and doctors. To this end, this paper proposes a new model called Att-BiRNN-Att which combines the Bidirectional RNN (Recurrent Neural Network) with two attention mechanisms. The model employs BiRNN to capture more information in the context instead of the traditional directional RNN. Also, two attention mechanisms are used in the model to produce better feature representation of the answer. One attention is used before the input of BiRNN, and the other is used after the output of BiRNN. The combination of two attentions makes full use of the relevant information between the answer and question. The experiment on the HealthTap medical QA dataset shows that our model outperforms four state-of-theart deep learning models, which confirm the effectiveness of Att-BiRNN-Att model.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Automated question answering in Webclopedia

2002 ◽  
Author(s):  
Ulf Hermjakob ◽  
Eduard Hovy ◽  
Chin-Yew Lin
Keyword(s):  
Question Answering

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

An Efficient Semantic Analysis Technique for the Question Answering Systems

2019 ◽  
Vol 14 (22) ◽  
pp. 8289-8292
Author(s):  
Ibrahim Mahmoud Ibrahim Alturani ◽  
Mohd Pouzi Bin Hamzah
Keyword(s):  
Question Answering ◽  
Semantic Analysis ◽  
Analysis Technique ◽  
Question Answering Systems

Toward a Question Answering Roadmap

2002 ◽  
Author(s):  
Mark T. Maybury
Keyword(s):  
Question Answering
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