论文标题
自然语言理解语料库中的否定分析
An Analysis of Negation in Natural Language Understanding Corpora
论文作者
论文摘要
本文分析了八个流行语料库中的否定，涵盖了六个自然语言理解任务。我们表明，与通用英语相比，这些语料库几乎没有否定，其中很少的否定通常并不重要。确实，人们通常可以忽略否定，但仍然做出正确的预测。此外，实验结果表明，经过这些语料库训练的最先进的变压器在包含否定的情况下获得了更糟糕的结果，尤其是在否定很重要的情况下。我们得出的结论是，需要新的否定语料库来解决存在否定时的自然语言理解任务。
This paper analyzes negation in eight popular corpora spanning six natural language understanding tasks. We show that these corpora have few negations compared to general-purpose English, and that the few negations in them are often unimportant. Indeed, one can often ignore negations and still make the right predictions. Additionally, experimental results show that state-of-the-art transformers trained with these corpora obtain substantially worse results with instances that contain negation, especially if the negations are important. We conclude that new corpora accounting for negation are needed to solve natural language understanding tasks when negation is present.
