scholarly journals Sequence to Sequence Model Performance for Education Chatbot

2019 ◽  
Vol 14 (24) ◽  
pp. 56 ◽  
Author(s):  
Kulothunkan Palasundram ◽  
Nurfadhlina Mohd Sharef ◽  
Nurul Amelina Nasharuddin ◽  
Khairul Azhar Kasmiran ◽  
Azreen Azman
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Model Performance ◽  
Neural Machine Translation ◽  
Class A ◽  
Great Progress ◽  
Artificial Intelli ◽  
Education Administrators ◽  
Generation Problem

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of op-timal settings of the various components of Seq2Seq model for natural an-swer generation problem is very limited. Additionally, there has been no ex-periments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experi-ments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a cu-rated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model.

scholarly journals ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

Molecules ◽  
2017 ◽  
Vol 22 (10) ◽  
pp. 1732 ◽  
Author(s):  
Renzhi Cao ◽  
Colton Freitas ◽  
Leong Chan ◽  
Miao Sun ◽  
Haiqing Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Neural Machine Translation

Bayesian Probability and Tanimoto Based Recurrent Neural Network for Question Answering System

2021 ◽  
Author(s):  
Veeraraghavan Jagannathan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Bayesian Probability ◽  
Significant Information ◽  
Continuous Increase ◽  
Question Answering System ◽  
Relevant Result ◽  
Proper Nouns ◽  
Different Types

Question Answering (QA) has become one of the most significant information retrieval applications. Despite that, most of the question answering system focused to increase the user experience in finding the relevant result. Due to the continuous increase of web content, retrieving the relevant result faces a challenging issue in the Question Answering System (QAS). Thus, an effective Question Classification (QC), and retrieval approach named Bayesian probability and Tanimoto-based Recurrent Neural Network (RNN) are proposed in this research to differentiate the types of questions more efficiently. This research presented an analysis of different types of questions with respect to the grammatical structures. Various patterns are identified from the questions and the RNN classifier is used to classify the questions. The results obtained by the proposed Bayesian probability and Tanimoto-based RNN showed that the syntactic categories related to the domain-specific types of proper nouns, numeral numbers, and the common nouns enable the RNN classifier to reveal better result for different types of questions. However, the proposed approach obtained better performance in terms of precision, recall, and F-measure with the values of 90.14, 86.301, and 90.936 using dataset-2.

scholarly journals Convolutional recurrent neural network with template based representation for complex question answering

2020 ◽  
Vol 10 (3) ◽  
pp. 2710
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Internal Representation ◽  
Semantic Matching ◽  
Softmax Classifier ◽  
Questions And Answers ◽  
Fully Connected ◽  
The Given ◽  
Complex Question

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

scholarly journals FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering

2017 ◽  
Author(s):  
Giuseppe Attardi ◽  
Antonio Carta ◽  
Federico Errica ◽  
Andrea Madotto ◽  
Ludovica Pannitto
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering

Neural Machine Translation Using Sequence Modeling

2021 ◽  
Vol 9 (8) ◽  
pp. 2153-2169
Author(s):  
N Revathi
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Term Memory ◽  
Sequence Modeling

Abstract: Language is a main mode of communication, and translation is a critical tool for understanding information in a foreign language. Without the help of human translators, machine translation allows users to absorb unfamiliar linguistic material. The main goal of this project is to create a practical language translation from English to Hindi. Given its relevance and potential in the English-Hindi translation, machine translation is an efficient way to turn content into a new language without employing people. Among all available translation machines, Neural Machine Translation (NMT) is one of the most efficient ways. So, in this case, we're employing Sequence to Sequence Modeling, which includes the Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM), and Encoder-Decoder methods. Deep Neural Network (DNN) comprehension and principles of deep learning, i.e. machine translation, are disclosed in the field of Natural Language Processing (NLP). In machine reclining techniques, DNN plays a crucial role. Keywords: Sequence to Sequence, Encoder-Decoder, Recurrent Neural Network, Long & Short term Memory, Deep Neural Network.

scholarly journals Multi-scale Hierarchical Residual Network for Dense Captioning

2019 ◽  
Vol 64 ◽  
pp. 181-196 ◽  
Author(s):  
Yan Tian ◽  
Xun Wang ◽  
Jiachen Wu ◽  
Ruili Wang ◽  
Bailin Yang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Feature Space ◽  
Image Feature ◽  
Feature Maps ◽  
Residual Network ◽  
Multi Scale ◽  
Residual Learning ◽  
Novel Approach ◽  
Great Progress

Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.

scholarly journals Neural Machine Translation using Recurrent Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1395-1400
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Neural Machine Translation ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Translation Systems

In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.

scholarly journals Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Sami Sosa ◽  
Saja Majeed Mohammed ◽  
Haider Hadi Abbas ◽  
Israa Al Barazanchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Structural Information ◽  
Sequential Data

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.

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