Automatic Conditional Generation of Personalized Social Media Short Texts
This work addresses the need for personalized text generation in applications like machine translation and dialogue systems, though it is incremental as it builds on existing neural network methods.
The authors tackled the problem of generating machine-like texts by proposing a conditional language model that incorporates Big Five Personality (BFP) feature vectors to produce human-like short texts, resulting in syntactically correct and semantically smooth outputs with discriminative personality styles.
Automatic text generation has received much attention owing to rapid development of deep neural networks. In general, text generation systems based on statistical language model will not consider anthropomorphic characteristics, which results in machine-like generated texts. To fill the gap, we propose a conditional language generation model with Big Five Personality (BFP) feature vectors as input context, which writes human-like short texts. The short text generator consists of a layer of long short memory network (LSTM), where a BFP feature vector is concatenated as one part of input for each cell. To enable supervised training generation model, a text classification model based convolution neural network (CNN) has been used to prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic computational model, our generated Chinese short texts exhibit discriminative personality styles, which are also syntactically correct and semantically smooth with appropriate emoticons. With combination of natural language generation with psychological linguistics, our proposed BFP-dependent text generation model can be widely used for individualization in machine translation, image caption, dialogue generation and so on.