Recently, prompt-based learning has become a very popular solution in many
Natural Language Processing (NLP) tasks by inserting a template into model
input, which converts the task into a cloze-style one to smoothing out
differences between the Pre-trained Language Model (PLM) and the current task.
But in the case of relation classification, it is difficult to map the masked
output to the relation labels because of its abundant semantic information,
e.g. org:founded_by''. Therefore, a pre-trained model still needs enough
labelled data to fit the relations. To mitigate this challenge, in this paper,
we present a novel prompt-based learning method, namely LabelPrompt, for the
relation classification task. It is an extraordinary intuitive approach by a
motivation: ``GIVE MODEL CHOICES!''. First, we define some additional tokens to
represent the relation labels, which regards these tokens as the verbalizer
with semantic initialisation and constructs them with a prompt template method.
Then we revisit the inconsistency of the predicted relation and the given
entities, an entity-aware module with the thought of contrastive learning is
designed to mitigate the problem. At last, we apply an attention query strategy
to self-attention layers to resolve two types of tokens, prompt tokens and
sequence tokens. The proposed strategy effectively improves the adaptation
capability of prompt-based learning in the relation classification task when
only a small labelled data is available. Extensive experimental results
obtained on several bench-marking datasets demonstrate the superiority of the
proposed LabelPrompt method, particularly in the few-shot scenario.

本文提出了一种新的基于提示的学习方法 LabelPrompt，用于关系分类任务，通过定义附加令牌来表示关系标签，并构建提示模板方法，以及设计一个实体感知模块和注意查询策略来解决模型中的一些挑战，极大地提高了在缺乏标注数据情况下利用提示学习的适应能力，特别是在少样本场景中的表现超过了其他几种方法。