Interpretable Screenplay Quality Assessment

The motion picture industry has now grown to a multibillion-dollar business worldwide (Lash and Zhao, 2016). Decisions in selecting movies to be produced is critical to the profitability and reputation of a movie studio (Vogel, 2014). However, determine factors for the financial and artistic success of a film remain unknown. Particularly, screenplay, which is known to be the initial development phase for a movie production, could largely affect the financial budget and quality of the final movie production. Thus, an objective and reliable tool to evaluate a screenplay is of vital importance to aid the green-lighting processes. Thus far, we find very limited studies that aim to measure the quality of the screenplays, and there is need for approaches which could systematically evaluate the quality of the screenplay. In many of these existing literature, researchers primarily investigated in using screenplay to predict the box office performance. (Yuan et al., 2018) has worked on the dynamics of research literature using a deep learning model. To predict the citation popularity, they introduced a RNN-LSTM architecture aligned with a set of features including 1. Intrinsic quality; 2. Aging effect; 3. Recency effect; 4. Matthew effect. Inspired by this previous work, we intend to explore some similar feature extraction techniques given the similarity in between movies scripts and academic. However, we argue that business success is only an indirect measurement for screenplay quality, where it may be affected by many other factors like budget limit, animation effects etc. To robustly assess the quality of a screenplay, we propose to perform the screenplay evaluation using two approaches. First, we aim to assess the quality of the writing by comparing awardwinning screenplays and those that are not. The success of award-winning scripts, which are recognized produced writings in professionalism, are largely dependent on their textual properties and story-lines. Similar to other screenplay researches, we define popularity as one other metric in measuring the quality of a screenplay, which often includes box office, user and expert ratings from online sites. We argue that our definitions of screenplay quality could lead to a more reliable and convincing proof for the success of a movie. In addition, there has been limited works done in this quality/box office performance prediction domain using a neural network approach. We aim to utilize some state-of-the art black box model like hierarchical attention mechanism, etc., and compare it with traditional feature based machine learning models. Lastly, we want to highlight that domain knowledge in screenplays may bring numerous benefits in selecting the most relevant textual properties for the prediction of script quality (Gunter, 2018). Such interpretable features may also offer opportunities for the enhanced understanding of important elements in high-quality movie scripts. Besides, we hope to automatically extract these meaningful characteristics to help human obtain a comprehensive assessment on screenplay in a more efficient way, i.e., the greenlighting process in the industry.