Lexicosyntactic Inference in Neural Models
Lexicosyntactic Inference in Neural Models
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DOI：
10.18653/v1/d18-1501
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发表时间：
2018-08
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通讯作者：
Aaron Steven White;Rachel Rudinger;Kyle Rawlins;Benjamin Van Durme
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文献类型：
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作者：
Aaron Steven White;Rachel Rudinger;Kyle Rawlins;Benjamin Van Durme
我们研究神经模型捕捉词汇句法推理的能力：词汇和句法信息的相互作用所触发的推理。本文以事件真实性预测为例，构建了一个包含英语小句嵌入动词的真实性判断数据集。我们使用我们公开提供的这个数据集来探测当前最先进的神经系统的行为，表明这些系统会产生某些系统性错误，这些错误通过事实预测的透镜清晰可见。
We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.