Text matching is a fundamental technique in both information retrieval and
natural language processing. Text matching tasks share the same paradigm that
determines the relationship between two given texts. The relationships vary
from task to task, e.g.~relevance in document retrieval, semantic alignment in
paraphrase identification and answerable judgment in question answering.
However, the essential signals for text matching remain in a finite scope,
i.e.~exact matching, semantic matching, and inference matching. Ideally, a good
text matching model can learn to capture and aggregate these signals for
different matching tasks to achieve competitive performance, while recent
state-of-the-art text matching models, e.g.~Pre-trained Language Models (PLMs),
are hard to generalize. It is because the end-to-end supervised learning on
task-specific dataset makes model overemphasize the data sample bias and
task-specific signals instead of the essential matching signals. To overcome
this problem, we adopt a specialization-generalization training strategy and
refer to it as Match-Prompt. In specialization stage, descriptions of different
matching tasks are mapped to a few prompt tokens. In generalization stage,
matching model explores the essential matching signals by being trained on
diverse matching tasks. High diverse matching tasks avoid model fitting the
data bias on a specific task, so that model can focus on learning the essential
matching signals. Meanwhile, the prompt tokens obtained in the first step help
the model distinguish different task-specific matching signals. Experimental
results on public datasets show that Match-Prompt can improve multi-task
generalization capability of PLMs in text matching and yield better in-domain
multi-task, out-of-domain multi-task and new task adaptation performance than
multi-task and task-specific models trained by previous fine-tuning paradigm.

本论文提出了一种特化 - 泛化训练策略 Match-Prompt，旨在帮助模型在不同的文本匹配任务中学习到本质的匹配信号，从而改善 Pre-trained Language Models 的多任务泛化能力和适应性。实验结果表明，Match-Prompt 在多个公共数据集上优于传统的 fine-tuning 策略。