Controlling Text Edition by Changing Answers of Specific Questions
This addresses the problem of precise text modification in domains like legal documents or news texts, but it is incremental as it builds on existing datasets and introduces new metrics.
The paper tackles the task of controllable text edition by modifying a long text to fit a target answer for a given question, achieving results that demonstrate their method is well-suited for this novel NLP task.
In this paper, we introduce the new task of controllable text edition, in which we take as input a long text, a question, and a target answer, and the output is a minimally modified text, so that it fits the target answer. This task is very important in many situations, such as changing some conditions, consequences, or properties in a legal document, or changing some key information of an event in a news text. This is very challenging, as it is hard to obtain a parallel corpus for training, and we need to first find all text positions that should be changed and then decide how to change them. We constructed the new dataset WikiBioCTE for this task based on the existing dataset WikiBio (originally created for table-to-text generation). We use WikiBioCTE for training, and manually labeled a test set for testing. We also propose novel evaluation metrics and a novel method for solving the new task. Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.