| Summary: | Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity recognition (NER), due to the sophisticated design of entity-related templates. Note that the nature of prompt tuning makes full use of the parameters of the mask language model (MLM) head, while previous methods solely utilized the last hidden layer of language models (LMs) and the power of the MLM head is overlooked. In this work, we discovered the characteristics of semantic feature changes in samples after being processed using MLMs. Based on this characteristic, we designed a prompt-tuning variant for NER tasks. We let the pre-trained model predict the label words derived from the training dataset at each position and fed the generated logits (non-normalized probability) to the CRF layer. We evaluated our method on three popular datasets, and the experiments showed that our proposed method outperforms the state-of-the-art model in all three Chinese datasets.
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