A Reading Comprehension Style Question Answering Model Based On Attention Mechanism

2018 ◽  
Author(s):  
Linlong Xiao ◽  
Nanzhi Wang ◽  
Guocai Yang
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Attention Mechanism ◽  
Model Based

scholarly journals Capturing Greater Context for Question Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 9065-9072
Author(s):  
Luu Anh Tuan ◽  
Darsh Shah ◽  
Regina Barzilay
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Dialogue Systems ◽  
Question Generation ◽  
Multi Stage ◽  
Art Methods ◽  
Relevant Context ◽  
Automatic Question Generation

Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension. While questions are often asked with respect to long documents, there are many challenges with modeling such long documents. Many existing techniques generate questions by effectively looking at one sentence at a time, leading to questions that are easy and not reflective of the human process of question generation. Our goal is to incorporate interactions across multiple sentences to generate realistic questions for long documents. In order to link a broad document context to the target answer, we represent the relevant context via a multi-stage attention mechanism, which forms the foundation of a sequence to sequence model. We outperform state-of-the-art methods on question generation on three question-answering datasets - SQuAD, MS MARCO and NewsQA. 1

Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism

2021 ◽  
Vol 20 (1) ◽  
pp. 1-21
Author(s):  
Peiying Zhang ◽  
Xingzhe Huang ◽  
Maozhen Li ◽  
Yu Xue
Keyword(s):  
Neural Networks ◽  
Customer Service ◽  
Language Processing ◽  
Question Answering ◽  
Hybrid Algorithms ◽  
Attention Mechanism ◽  
Similarity Analysis ◽  
Model Based ◽  
Sentence Similarity ◽  
Nature Inspired Algorithms

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

scholarly journals Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiaohui Tang
Keyword(s):  
Neural Network ◽  
Reading Comprehension ◽  
Question Answering ◽  
Test Analysis ◽  
Model Based ◽  
English Reading ◽  
Comprehension Ability ◽  
Low Efficiency ◽  
Machine Reading ◽  
Strong Reading

In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

scholarly journals EXPLORING THE BERT CROSS-LINGUAL TRANSFER FOR READING COMPREHENSION

2020 ◽  
Author(s):  
V. P. Konovalov ◽  
◽  
P. A. Gulyaev ◽  
A. A. Sorokin ◽  
Y. M. Kuratov ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
English Language ◽  
Question Answering ◽  
Specific Data ◽  
Model Based ◽  
Comprehension Task ◽  
The One ◽  
Cross Lingual ◽  
Cross Language

Multilingual BERT has been shown to generalize well in a zero-shot crosslingual setting. This generalization was measured on POS and NER tasks. We explore the multilingual BERT cross-language transferability on the reading comprehension task. We compare different modes of training of question-answering model for a non-English language using both English and language-specific data. We demonstrate that the model based on multilingual BERT is slightly behind the monolingual BERT-based on Russian data, however, it achieves comparable results with the language-specific variant on Chinese. We also show that training jointly on English data and additional 10,000 monolingual samples allows it to reach the performance comparable to the one trained on monolingual data only.

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

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