Adobe Inc.
Detecting affective characteristics of text with gated convolutional encoder-decoder framework

Abstract:

Certain embodiments involve using a gated convolutional encoder-decoder framework for applying affective characteristic labels to input text. For example, a method for identifying an affect label of text with a gated convolutional encoder-decoder model includes receiving, at an encoder, input text. The method also includes encoding the input text to generate a latent representation of the input text. Additionally, the method includes receiving, at a supervised classification engine, extracted linguistic features of the input text and the latent representation of the input text. Further, the method includes predicting an affect characterization of the input text using the extracted linguistic features and the latent representation. Furthermore, the method includes identifying an affect label of the input text using the predicted affect characterization. The gated convolutional encoder-decoder model is jointly trained using a weighted auto-encoder loss associated with a reconstruction engine and a weighted classification loss associated with the supervised classification engine.