MRC-PASCL: A Few-Shot Machine Reading Comprehension Approach via Post-Training and Answer Span-Oriented Contrastive Learning

IF 4.1 2区 计算机科学 Q1 ACOUSTICS IEEE/ACM Transactions on Audio, Speech, and Language Processing Pub Date : 2024-10-31 DOI:10.1109/TASLP.2024.3490373
Ren Li;Qiao Xiao;Jianxi Yang;Luyi Zhang;Yu Chen
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Abstract

The rapid development of pre-trained language models (PLMs) has significantly enhanced the performance of machine reading comprehension (MRC). Nevertheless, the traditional fine-tuning approaches necessitate extensive labeled data. MRC remains a challenging task in the few-shot settings or low-resource scenarios. This study proposes a novel few-shot MRC approach via post-training and answer span-oriented contrastive learning, termed MRC-PASCL. Specifically, in the post-training module, a novel noun-entity-aware data selection and generation strategy is proposed according to characteristics of MRC task and data, focusing on masking nouns and named entities in the context. In terms of fine-tuning, the proposed answer span-oriented contrastive learning manner selects spans around the golden answers as negative examples, and performs multi-task learning together with the standard MRC answer prediction task. Experimental results show that MRC-PASCL outperforms the PLMs-based baseline models and the 7B and 13B large language models (LLMs) cross most MRQA 2019 datasets. Further analyses show that our approach achieves better inference efficiency with lower computational resource requirement. The analysis results also indicate that the proposed method can better adapt to the domain-specific scenarios.
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MRC-PASCL:通过后训练和以答案跨度为导向的对比学习实现快速机器阅读理解的方法
预训练语言模型(PLM)的快速发展大大提高了机器阅读理解(MRC)的性能。然而,传统的微调方法需要大量标注数据。在少量数据或资源匮乏的情况下,MRC 仍然是一项具有挑战性的任务。本研究提出了一种通过后训练和以答案跨度为导向的对比学习(称为 MRC-PASCL)来实现的新颖的少量 MRC 方法。具体来说,在后训练模块中,根据 MRC 任务和数据的特点,提出了一种新颖的名词实体感知数据选择和生成策略,重点是屏蔽上下文中的名词和命名实体。在微调方面,提出了以答案跨度为导向的对比学习方式,选择黄金答案周围的跨度作为负例,与标准 MRC 答案预测任务一起执行多任务学习。实验结果表明,MRC-PASCL 在大多数 MRQA 2019 数据集上的表现优于基于 PLMs 的基线模型以及 7B 和 13B 大语言模型(LLMs)。进一步的分析表明,我们的方法以更低的计算资源需求实现了更好的推理效率。分析结果还表明,所提出的方法能更好地适应特定领域的场景。
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来源期刊
IEEE/ACM Transactions on Audio, Speech, and Language Processing
IEEE/ACM Transactions on Audio, Speech, and Language Processing ACOUSTICS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
11.30
自引率
11.10%
发文量
217
期刊介绍: The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them. In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement. In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.
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