论文标题
利用否定的肯定解释改善了自然语言的理解
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding
论文作者
论文摘要
否定在许多自然语言理解任务中构成了挑战。受到理解的启发，理解否定的陈述通常要求人类推断肯定的解释，在本文中，我们表明，这样做有利于三个自然语言理解任务的模型。我们提出了一个自动化程序，以收集否定的句子及其肯定的解释，导致超过15万对。实验结果表明，利用这些对有助于（a）T5在以前的基准中产生否定的肯定解释，并且（b）基于罗伯塔的分类器解决了自然语言推断的任务。我们还利用我们的配对来构建一个插头的神经发电机，该神经发电机给定否定的陈述产生了肯定的解释。然后，我们将验证的发电机纳入基于罗伯塔的分类器进行情感分析，并表明这样做可以改善结果。至关重要的是，我们的建议不需要任何手动努力。
Negation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks. We present an automated procedure to collect pairs of sentences with negation and their affirmative interpretations, resulting in over 150,000 pairs. Experimental results show that leveraging these pairs helps (a) T5 generate affirmative interpretations from negations in a previous benchmark, and (b) a RoBERTa-based classifier solve the task of natural language inference. We also leverage our pairs to build a plug-and-play neural generator that given a negated statement generates an affirmative interpretation. Then, we incorporate the pretrained generator into a RoBERTa-based classifier for sentiment analysis and show that doing so improves the results. Crucially, our proposal does not require any manual effort.
