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arXiv · 2205.05128

Human Language Modeling

Nikita Soni·Matthew Matero·Niranjan Balasubramanian·H. Andrew Schwartz
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Abstract

Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a human-level exists to connect sequences of documents (e.g. social media messages) and capture the notion that human language is moderated by changing human states. We introduce, HaRT, a large-scale transformer model for the HuLM task, pre-trained on approximately 100,000 social media users, and demonstrate its effectiveness in terms of both language modeling (perplexity) for social media and fine-tuning for 4 downstream tasks spanning document- and user-levels: stance detection, sentiment classification, age estimation, and personality assessment. Results on all tasks meet or surpass the current state-of-the-art.

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Nikita Soni, Matthew Matero, Niranjan Balasubramanian, H. Andrew Schwartz. 2022-05-10. Human Language Modeling. https://doi.org/10.18653/v1%2F2022.findings-acl.52

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