Tweet to News Conversion: An Investigation into Unsupervised Controllable Text Generation
This addresses the challenge of unsupervised controllable text generation for NLP applications, though it is incremental as it builds on existing style transfer and stitching methods.
The paper tackled the problem of generating coherent news paragraphs from disaster domain tweets without parallel data, achieving a BLEU score of 19.32 in an unsupervised setting.
Text generator systems have become extremely popular with the advent of recent deep learning models such as encoder-decoder. Controlling the information and style of the generated output without supervision is an important and challenging Natural Language Processing (NLP) task. In this paper, we define the task of constructing a coherent paragraph from a set of disaster domain tweets, without any parallel data. We tackle the problem by building two systems in pipeline. The first system focuses on unsupervised style transfer and converts the individual tweets into news sentences. The second system stitches together the outputs from the first system to form a coherent news paragraph. We also propose a novel training mechanism, by splitting the sentences into propositions and training the second system to merge the sentences. We create a validation and test set consisting of tweet-sets and their equivalent news paragraphs to perform empirical evaluation. In a completely unsupervised setting, our model was able to achieve a BLEU score of 19.32, while successfully transferring styles and joining tweets to form a meaningful news paragraph.