2021
ACL
ACL 2021
Human Perception in Natural Language Generation
Abstract
AbstractWe ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, and observe that this fine-tuned model produces texts that are indeed perceived more human-like than the original model. Contextually, we show that our automatic evaluation strategy well correlates with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.
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Interdisciplinary Bridge
— Deep Learning and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— text quality evaluation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Data Augmentation
Deep Learning > Models > Generative Models
Natural Language Processing > Generation > Language Modeling
Natural Language Processing > Generation > Text Generation
Natural Language Processing > Resources & Methods > Large Language Models
Interdisciplinary > Linguistics > Computational Linguistics
Interdisciplinary > Cognitive Science > Perception