论文标题
通过以否定为中心的预培训来改善否定检测
Improving negation detection with negation-focused pre-training
论文作者
论文摘要
否定是一种常见的语言特征，在许多语言理解任务中至关重要，但是由于其在不同类型的文本中的表达方式多样性，它仍然是一个困难的问题。最近的工作表明，在各种任务中包含否定的样本上的最新NLP模型表现不佳，而否定检测模型在跨域中的转移不能很好地传递。我们提出了一种新的以否定为重点的预训练策略，涉及针对性的数据扩展和否定掩盖，以更好地将否定信息纳入语言模型。对共同基准测试的广泛实验表明，我们提出的方法改善了对强基线Negbert（Khandewal and Sawant，2020）的否定检测性能和概括性。
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent work has shown that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation detection models do not transfer well across domains. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking, to better incorporate negation information into language models. Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT (Khandewal and Sawant, 2020).
