PTR: Prompt Tuning with Rules for Text Classification

• URL: http://arxiv.org/abs/2105.11259v1
• Date: Mon, 24 May 2021 13:24:02 GMT
• Title: PTR: Prompt Tuning with Rules for Text Classification
• Authors: Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, Maosong Sun
• Abstract summary: Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. We propose prompt tuning with rules (PTR) for many-class text classification. PTR is able to encode prior knowledge of each class into prompt tuning.
• Score: 64.1655047016891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream task. Prompt tuning has achieved promising results on some few-class classification tasks such as sentiment classification and natural language inference. However, manually designing lots of language prompts is cumbersome and fallible. For those auto-generated prompts, it is also expensive and time-consuming to verify their effectiveness in non-few-shot scenarios. Hence, it is challenging for prompt tuning to address many-class classification tasks. To this end, we propose prompt tuning with rules (PTR) for many-class text classification, and apply logic rules to construct prompts with several sub-prompts. In this way, PTR is able to encode prior knowledge of each class into prompt tuning. We conduct experiments on relation classification, a typical many-class classification task, and the results on benchmarks show that PTR can significantly and consistently outperform existing state-of-the-art baselines. This indicates that PTR is a promising approach to take advantage of PLMs for those complicated classification tasks.

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