119,254 research outputs found
Learning a Policy for Opportunistic Active Learning
Active learning identifies data points to label that are expected to be the
most useful in improving a supervised model. Opportunistic active learning
incorporates active learning into interactive tasks that constrain possible
queries during interactions. Prior work has shown that opportunistic active
learning can be used to improve grounding of natural language descriptions in
an interactive object retrieval task. In this work, we use reinforcement
learning for such an object retrieval task, to learn a policy that effectively
trades off task completion with model improvement that would benefit future
tasks.Comment: EMNLP 2018 Camera Read
Storytelling with objects to explore digital archives
Finding media in archives is difficult while storytelling with photos can be fun and supports memory retrieval. Could the search for media become a natural part of the storytelling experience? This study investigates spatial interactions with objects as a means to encode information for retrieval while being embedded in the story flow. An experiment is carried out in which participants watch a short video and re-tell the story using cards each of which shows a character or object occurring in the video. Participants arrange the cards when telling the story. It is analyzed what information interactions with cards carry and how this information relates to the language of storytelling. Most participants align interactions with objects with the sentences of the story while some arrange the cards corresponding to the video scene. Spatial interactions with objects can carry information on their own or complemented by language
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