System including an artificial neural network for image editing, and comprising: a word embedding component configured to convert text input into a set of word vectors; a feature encoder of the ANN configured to create a combined feature vector for the text input based on the word vectors; a scoring layer of the ANN configured to compute labelling scores based on the combined feature vector; the feature encoder and/or scoring layer are trained using multi-task learning with a loss function including at least two loss values, one of which is based on mutual information, context-based prediction, or sentence-based prediction; a command component configured to identify an image editing command based on the labelling scores; and an image editing application configured to edit an image based on the command. The feature encoder may comprise a word encoder, which may comprise a 2-layer bi-directional LSTM, configured to compute a word-based feature vector and a structural encoder, which may comprise a graph convolutional network, configured to compute a structure-based feature vector, wherein the combined feature vector comprises the word-based and structure-based feature vectors. Also disclosed are a method for training a neural network and a method for image editing.
Slot filling with contextual information