A Deep Neural Network Framework for English Hindi Question Answering

2020 ◽  
Vol 19 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Deepak Gupta ◽  
Asif Ekbal ◽  
Pushpak Bhattacharyya
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering

scholarly journals An Empirical Study on Deep Neural Network Models for Chinese Dialogue Generation

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1756
Author(s):  
Zhe Li ◽  
Mieradilijiang Maimaiti ◽  
Jiabao Sheng ◽  
Zunwang Ke ◽  
Wushour Silamu ◽  
...  
Keyword(s):  
Neural Network ◽  
Empirical Study ◽  
Deep Neural Network ◽  
Question Answering ◽  
State Of The Art ◽  
Network Models ◽  
Superior Performance ◽  
Research Progress ◽  
Neural Network Models ◽  
Wide Range

The task of dialogue generation has attracted increasing attention due to its diverse downstream applications, such as question-answering systems and chatbots. Recently, the deep neural network (DNN)-based dialogue generation models have achieved superior performance against conventional models utilizing statistical machine learning methods. However, despite that an enormous number of state-of-the-art DNN-based models have been proposed, there lacks detailed empirical comparative analysis for them on the open Chinese corpus. As a result, relevant researchers and engineers might find it hard to get an intuitive understanding of the current research progress. To address this challenge, we conducted an empirical study for state-of-the-art DNN-based dialogue generation models in various Chinese corpora. Specifically, extensive experiments were performed on several well-known single-turn and multi-turn dialogue corpora, including KdConv, Weibo, and Douban, to evaluate a wide range of dialogue generation models that are based on the symmetrical architecture of Seq2Seq, RNNSearch, transformer, generative adversarial nets, and reinforcement learning respectively. Moreover, we paid special attention to the prevalent pre-trained model for the quality of dialogue generation. Their performances were evaluated by four widely-used metrics in this area: BLEU, pseudo, distinct, and rouge. Finally, we report a case study to show example responses generated by these models separately.

scholarly journals A Novel Framework Using Zero Shot Learning Technique for a Non-Factoid Question Answering System

2021 ◽  
Vol 16 (6) ◽  
pp. 1-13
Author(s):  
Akila Devi T. R. ◽  
K. Javubar Sathick ◽  
A. Abdul Azeez Khan ◽  
L. Arun Raj
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering ◽  
Neural Network Classifier ◽  
Multiple Sources ◽  
Question Answering System ◽  
Passage Level ◽  
Learning Technique ◽  
Natural Language Query ◽  
Main Components

Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.

scholarly journals A Novel Framework using Zero Shot Learning Technique for Non-Factoid Question Answering System

2021 ◽  
Vol 16 (6) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering ◽  
Neural Network Classifier ◽  
Multiple Sources ◽  
Question Answering System ◽  
Passage Level ◽  
Learning Technique ◽  
Natural Language Query ◽  
Main Components

Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.

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