Predicting the Focus of Negation: Model and Error Analysis
Predicting the Focus of Negation: Model and Error Analysis
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DOI：
10.18653/v1/2020.acl-main.743
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Md Mosharaf Hossain;Kathleen E. Hamilton;Alexis Palmer;Eduardo Blanco
中科院分区：
文献类型：
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作者：
Md Mosharaf Hossain;Kathleen E. Hamilton;Alexis Palmer;Eduardo Blanco
否定的焦点是一组被否定的符号，是揭示否定话语的肯定替代方案的关键组成部分。在本文中，我们尝试用神经网络来预测否定的焦点。我们主要的新颖之处是利用范围检测器引入否定范围作为网络的额外输入。实验结果表明，该方法获得了迄今为止最好的效果。此外，我们执行详细的错误分析，提供对主要错误类别的洞察，并根据模型是否考虑范围和上下文信息来分析错误。
The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.