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Ellipsis Resolution as Question Answering: An Evaluation

机译:省略号解决问题应答:评估

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摘要

Most, if not all forms of ellipsis (e.g., 'so does Mary') are similar to reading comprehension questions ('what does Mary do'), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and corefer-ence resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).
机译:大多数情况下,如果不是所有形式的省略号(例如,玛丽')类似于阅读理解问题('Mary Do'),在这是为了解决它们,我们需要确定适当的文本跨度 前面的话语。 在此观察之后,我们提出了一种依赖于为问题应答(QA)开发的架构的英语省略号解决方案的替代方法。 我们介绍了单任务模型,以及在辅助QA和Corefer-Ence分辨率数据集上培训的联合模型,显然优于闸门省略技术的当前状态(从70.00到86.01 f1)和动词短语省略(从72.89到78.66 F1) )。

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