Leveraging Quality Prediction Models for Automatic Writing Feedback
This work addresses the need for better automated feedback to enhance content quality on social media platforms, representing a domain-specific incremental advance.
The paper tackled the problem of providing automated writing feedback for user-generated content by combining quality prediction models with explanation techniques to identify features that improve text quality, resulting in a >14% improvement in writing quality on Amazon and Airbnb platforms, which is a 3X improvement over recent techniques.
User-generated, multi-paragraph writing is pervasive and important in many social media platforms (i.e. Amazon reviews, AirBnB host profiles, etc). Ensuring high-quality content is important. Unfortunately, content submitted by users is often not of high quality. Moreover, the characteristics that constitute high quality may even vary between domains in ways that users are unaware of. Automated writing feedback has the potential to immediately point out and suggest improvements during the writing process. Most approaches, however, focus on syntax/phrasing, which is only one characteristic of high-quality content. Existing research develops accurate quality prediction models. We propose combining these models with model explanation techniques to identify writing features that, if changed, will most improve the text quality. To this end, we develop a perturbation-based explanation method for a popular class of models called tree-ensembles. Furthermore, we use a weak-supervision technique to adapt this method to generate feedback for specific text segments in addition to feedback for the entire document. Our user study finds that the perturbation-based approach, when combined with segment-specific feedback, can help improve writing quality on Amazon (review helpfulness) and Airbnb (host profile trustworthiness) by > 14% (3X improvement over recent automated feedback techniques).