scholarly journals Building a Discourse-Argument Hybrid System for Vietnamese Why-Question Answering

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chinh Trong Nguyen ◽  
Dang Tuan Nguyen
Keyword(s):  
Deep Learning ◽  
Discourse Analysis ◽  
Natural Language ◽  
Question Answering ◽  
Learning Model ◽  
Test Results ◽  
Sentence Level ◽  
Final Answer ◽  
Why Questions ◽  
Deep Learning Model

Recently, many deep learning models have archived high results in question answering task with overall F1 scores above 0.88 on SQuAD datasets. However, many of these models have quite low F1 scores on why-questions. These F1 scores range from 0.57 to 0.7 on SQuAD v1.1 development set. This means these models are more appropriate to the extraction of answers for factoid questions than for why-questions. Why-questions are asked when explanations are needed. These explanations are possibly arguments or simply subjective opinions. Therefore, we propose an approach to finding the answer for why-question using discourse analysis and natural language inference. In our approach, natural language inference is applied to identify implicit arguments at sentence level. It is also applied in sentence similarity calculation. Discourse analysis is applied to identify the explicit arguments and the opinions at sentence level in documents. The results from these two methods are the answer candidates to be selected as the final answer for each why-question. We also implement a system with our approach. Our system can provide an answer for a why-question and a document as in reading comprehension test. We test our system with a Vietnamese translated test set which contains all why-questions of SQuAD v1.1 development set. The test results show that our system cannot beat a deep learning model in F1 score; however, our system can answer more questions (answer rate of 77.0%) than the deep learning model (answer rate of 61.0%).

scholarly journals Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction

2021 ◽  
Author(s):  
Soo-Han Kang ◽  
Ji-Hyeong Han
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Vision System ◽  
Robot Vision ◽  
Learning Model ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Video Captioning ◽  
Global Action ◽  
Deep Learning Model

AbstractRobot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system

scholarly journals MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dhruv Sharma ◽  
Sanjay Purushotham ◽  
Chandan K. Reddy
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Medical Images ◽  
Learning Model ◽  
Training Data ◽  
Human Errors ◽  
Medical Practitioners ◽  
Visual Question Answering ◽  
Deep Learning Model ◽  
Real World Problems

AbstractMedical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction—categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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