PTR: Prompt Tuning with Rules for Text Classification

TL;DR

This paper introduces PTR, a prompt tuning method with logic rules that encodes prior knowledge for many-class text classification, significantly improving performance over existing methods.

Contribution

The paper proposes a novel prompt tuning approach using logic rules to incorporate prior knowledge, addressing challenges in many-class classification tasks.

Findings

PTR outperforms state-of-the-art baselines on relation classification

Encoding prior knowledge improves prompt tuning effectiveness

Method is effective for complex many-class tasks

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 tasks. 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 still 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 and complicated many-class classification task, and the results 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 both human prior knowledge and PLMs for those complicated classification tasks.