An Analysis of Negation in Natural Language Understanding Corpora
An Analysis of Negation in Natural Language Understanding Corpora
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DOI：
10.48550/arxiv.2203.08929
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Md Mosharaf Hossain;Dhivya Chinnappa;Eduardo Blanco
中科院分区：
文献类型：
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作者：
Md Mosharaf Hossain;Dhivya Chinnappa;Eduardo Blanco
本文分析了六个自然语言理解任务中的八个热门语料库中的否定现象。我们发现，与通用英语相比，这些语料库中的否定词很少，而且其中的几个否定词往往是不重要的。事实上，人们经常可以忽略否定，但仍然可以做出正确的预测。此外，实验结果表明，使用这些语料库训练的最先进的转换器在包含否定的实例中获得的结果要差得多，特别是当否定是重要的时候。我们的结论是，当存在否定时，需要新的考虑否定的语料库来解决自然语言理解任务。
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.