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Found 7 papers, 1 papers with code
Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization
Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals.
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles.
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally, using only a measure of task performance as feedback, can violate societal norms for acceptable behavior or cause harm.
Influencing Reinforcement Learning through Natural Language Guidance
In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known Interactive reinforcement learning technique.
Visual Question Answering Using Semantic Information from Image Descriptions
In this work, we propose a deep neural architecture that uses an attention mechanism which utilizes region based image features, the natural language question asked, and semantic knowledge extracted from the regions of an image to produce open-ended answers for questions asked in a visual question answering (VQA) task.
Learning Norms from Stories: A Prior for Value Aligned Agents
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans.
