Learning from Explanations: Multi-aspect based Age-restricted Rating Prediction in Long Scripts
In the Motion Picture Association of America (MPAA), reviewers watch the entire film to determine the age-restricted category (MPAA rating) of the movie and provide the explanatory feedback for rating decision. As such human expert system is a time-consuming and non-scalable process, this paper proposes to develop a machine review system named MARS that automatically predicts the MPAA ratings of movie scripts. Specifically, in MARS, we first explore the use of the well-studied multi-aspect classification as machine-provided explanations, then leverage them to better learn the target rating prediction models. We demonstrate MARS outperforms various baselines by around 10 points in terms of F1 score, detecting severe contents with multi-aspect view.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations
Explainable AI (XAI) methods aim to describe the decision process of deep neural networks. Early XAI methods produced visual explanations, whereas more recent techniques generate multimodal explanations that include text…
Activity PredictionActivity RecognitionHybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to …
Explainable RecommendationCoKe: Customizable Fine-Grained Story Evaluation via Chain-of-Keyword Rationalization
Evaluating creative text such as human-written stories using language models has always been a challenging task -- owing to the subjectivity of multi-annotator ratings. To mimic the thinking process of humans, chain of t…
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence
The societal and ethical implications of the use of opaque artificial intelligence systems for consequential decisions, such as welfare allocation and criminal justice, have generated a lively debate among multiple stake…
Explainable artificial intelligenceELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent recommendations, generating textual exp…
Collaborative FilteringText Generation