Leveraging Pre-Trained Language Models to Streamline Natural Language
Interaction for Self-Tracking
- URL: http://arxiv.org/abs/2205.15503v2
- Date: Wed, 1 Jun 2022 01:52:31 GMT
- Title: Leveraging Pre-Trained Language Models to Streamline Natural Language
Interaction for Self-Tracking
- Authors: Young-Ho Kim, Sungdong Kim, Minsuk Chang, Sang-Woo Lee
- Abstract summary: We propose a novel NLP task for self-tracking that extracts close- and open-ended information from a retrospective activity log.
The framework augments the prompt using synthetic samples to transform the task into 10-shot learning, to address a cold-start problem in bootstrapping a new tracking topic.
- Score: 25.28975864365579
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Current natural language interaction for self-tracking tools largely depends
on bespoke implementation optimized for a specific tracking theme and data
format, which is neither generalizable nor scalable to a tremendous design
space of self-tracking. However, training machine learning models in the
context of self-tracking is challenging due to the wide variety of tracking
topics and data formats. In this paper, we propose a novel NLP task for
self-tracking that extracts close- and open-ended information from a
retrospective activity log described as a plain text, and a domain-agnostic,
GPT-3-based NLU framework that performs this task. The framework augments the
prompt using synthetic samples to transform the task into 10-shot learning, to
address a cold-start problem in bootstrapping a new tracking topic. Our
preliminary evaluation suggests that our approach significantly outperforms the
baseline QA models. Going further, we discuss future application domains toward
which the NLP and HCI researchers can collaborate.
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