论文标题
多跨样式的提取用于生成阅读理解
Multi-span Style Extraction for Generative Reading Comprehension
论文作者
论文摘要
生成机器阅读理解（MRC）需要一个模型来生成良好的答案。对于这种类型的MRC，答案生成方法对于模型性能至关重要。但是，通常表现不佳的生成模型应该是任务的正确模型。同时，已经证明单跨度提取模型对提取MRC有效，在该段落中，答案被约束至段落中的单个跨度。然而，当应用于生成的MRC时，他们通常会因产生不完整的答案或引入多余的单词而受苦。因此，我们将单跨提取方法扩展到多跨度，提出了一个新框架，该框架可以使生成性MRC平稳求解为多跨度提取。彻底的实验表明，这种新颖的方法可以减轻生成模型和单跨模型之间的困境，并以更好的语法和语义产生答案。
Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for the task, in generally perform poorly. At the same time, single-span extraction models have been proven effective for extractive MRC, where the answer is constrained to a single span in the passage. Nevertheless, they generally suffer from generating incomplete answers or introducing redundant words when applied to the generative MRC. Thus, we extend the single-span extraction method to multi-span, proposing a new framework which enables generative MRC to be smoothly solved as multi-span extraction. Thorough experiments demonstrate that this novel approach can alleviate the dilemma between generative models and single-span models and produce answers with better-formed syntax and semantics.
