Gated Convolutional Encoder-Decoder for Semi-supervised Affect Prediction
Analyzing human reactions from text is an important step towards automated modeling of affective content.The variance in human perceptions and experiences leads to a lack of uniform,well-labeled,ground-truth datasets,hence,limiting the scope of neural supervised learning approaches.Recurrent and convolutional networks are popular for text classification and generation tasks,specifically,where large datasets are available; but are inefficient when dealing with unlabeled corpora.We propose a gated sequence-to-sequence,convolutionaldeconvolutional autoencoding(GCNN-DCNN)framework for affect classification with limited labeled data.We show that compared to a vanilla CNN-DCNN network,gated networks improve performance for affect prediction as well as text reconstruction.We present a regression analysis comparing outputs of traditional learning models with information captured by hidden variables in the proposed network.Quantitative evaluation with joint,pre-trained networks,augmented with psycholinguistic features,reports highest accuracies for affect prediction,namely frustration,formality,and politeness in text.
Kushal Chawla Sopan Khosla Niyati Chhaya
Big Data Experience Lab,Adobe Research,Bangalore,India
国际会议
澳门
英文
237-250
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）