• DOI:10.1145/3511808.3557388
• Corpus ID: 247996942

Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning

@article{Xu2022MatchPromptIM,
  title={Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning},
  author={Shicheng Xu and Liang Pang and Huawei Shen and Xueqi Cheng},
  journal={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  year={2022}
}

• Shicheng Xu, Liang Pang, +1 author Xueqi Cheng
• Published 6 April 2022
• Computer Science
• Proceedings of the 31st ACM International Conference on Information & Knowledge Management

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, which ruins the generalization of model to different tasks. 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 only a few prompt tokens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks. High diverse matching tasks avoid model fitting the data sample 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 are added to the corresponding tasks to help the model distinguish different task-specific matching signals, as well as to form the basis prompt tokens for a new matching task. In this paper, we consider five common text matching tasks including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. Experimental results on eighteen 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.