Visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to ach...Visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks.Analysis of all features may cause information redundancy and heavy computational burden.Attention mechanism is a wise way to solve this problem.However,using single attention mechanism may cause incomplete concern of features.This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method.In the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as compensation.For the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall model.The results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.展开更多
Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the...Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.展开更多
In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with a...In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with attention mechanism to construct models to improve their accuracy. However, a single attention mechanism does not fully express the meaning of the text. In this paper, recurrent neural network is replaced with the convolutional neural network to process the text, and a superimposed attention mechanism is proposed. The model was constructed by combining a convolutional neural network with a superimposed attention mechanism. It shows that good results are achieved on the Stanford question answering dataset (SQuAD).展开更多
Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on t...Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on the question-answer joint learning(ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer(Q&A)pairs.Convolutional Neural Network(CNN)and Long Short-term Memory Network(LSTM)are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides,answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models,the ACLSTM model can effectively improve the prediction effect of answer quality.In particular,the mediumquality answer prediction,and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning,the Q&A pairs can better measure the semantic match between each other,fully reflecting the model’s superior performance in the semantic information processing of the answer text.展开更多
基金This work was supported by the Sichuan Science and Technology Program(2021YFQ0003).
文摘Visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks.Analysis of all features may cause information redundancy and heavy computational burden.Attention mechanism is a wise way to solve this problem.However,using single attention mechanism may cause incomplete concern of features.This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method.In the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as compensation.For the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall model.The results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61572326,and Grant 61802258the Natural Science Foundation of Shanghai under Grant 18ZR1428300the Shanghai Committee of Science and Technology under Grant 17070502800 and Grant 16JC1403000.
文摘Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.
文摘In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with attention mechanism to construct models to improve their accuracy. However, a single attention mechanism does not fully express the meaning of the text. In this paper, recurrent neural network is replaced with the convolutional neural network to process the text, and a superimposed attention mechanism is proposed. The model was constructed by combining a convolutional neural network with a superimposed attention mechanism. It shows that good results are achieved on the Stanford question answering dataset (SQuAD).
基金the Zhejiang Provincial Natural Science Foundation of China under Grant No.LGF18F020011.
文摘Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on the question-answer joint learning(ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer(Q&A)pairs.Convolutional Neural Network(CNN)and Long Short-term Memory Network(LSTM)are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides,answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models,the ACLSTM model can effectively improve the prediction effect of answer quality.In particular,the mediumquality answer prediction,and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning,the Q&A pairs can better measure the semantic match between each other,fully reflecting the model’s superior performance in the semantic information processing of the answer text.
