When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed, emergent errors can be hard to identify in prediction run-time and impossible to trace back to their sources. To address such gaps, in this paper we propose an error detection framework for sentiment analysis based on explainable features. We perform global-level feature validation with human-in-the-loop assessment, followed by an integration of global and local-level feature contribution analysis. Experimental results show that, given limited human-in-the-loop intervention, our method is able to identify erroneous model predictions on unseen data with high precision.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisSimilar Papers 제목 키워드 기반
When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prio…
Sentiment AnalysisTrial without Error: Towards Safe Reinforcement Learning via Human Intervention
AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious ha…
Atari Gamesreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Training Models to Detect Successive Robot Errors from Human Reactions
As robots become more integrated into society, detecting robot errors is essential for effective human-robot interaction (HRI). When a robot fails repeatedly, how can it know when to change its behavior? Humans naturally…
ActiveAED: A Human in the Loop Improves Annotation Error Detection
Manually annotated datasets are crucial for training and evaluating Natural Language Processing models. However, recent work has discovered that even widely-used benchmark datasets contain a substantial number of erroneo…
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: humans creat…
Reading Comprehension