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Extending Neural Question Answering with Linguistic Input Features

机译:使用语言输入功能扩展神经问答

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Considerable progress in neural question answering has been made on competitive general domain datasets. In order to explore methods to aid the generalization potential of question answering models, wc reimplement a state-of-the-art architecture, perform a parameter search on an open-domain dataset and cvaluate a first approach for integrating linguistic input features such as part-of-speech tags, syntactic dependency relations and semantic roles. The results show that adding these input features has a greater impact on performance than any of the architectural parameters we explore. Our findings suggest that these layers of linguistic knowledge have the potential to substantially increase the generalization capacities of neural QA models, thus facilitating cross-domain model transfer or the development of domain-agnostic QA models.
机译:在竞争性的通用领域数据集上,神经问题解答已经取得了可观的进步。为了探索有助于问答模型泛化潜力的方法,wc重新实现了最先进的体系结构,在开放域数据集上执行了参数搜索,并评估了集成语言输入特征(例如part)的第一种方法语音标记,句法依存关系和语义角色。结果表明,与我们探索的任何体系结构参数相比,添加这些输入功能对性能的影响更大。我们的发现表明,这些语言知识层有可能显着增加神经质量保证模型的泛化能力,从而促进跨域模型转移或与领域无关的质量保证模型的开发。

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