单跨任务提法对抽取式问答的重要性

Marie-Anne Xu, Rahul Khanna
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摘要

最近在机器阅读理解和问答方面的进展使机器能够达到甚至超越人类的问答能力。然而,这些问题中的大多数只有一个答案,而更多的有多个答案的问题或多跨度问题的测试尚未应用。因此,我们引入了一个新编译的数据集,该数据集由来自先前存在的数据集的带有多个答案的问题组成。此外,我们在构建的数据集上运行基于bert的预训练问答模型,以评估他们的阅读理解能力。在我们运行的三个基于bert的模型中,RoBERTa表现出最高的一致性性能,无论大小如何。我们发现,与单跨度源数据集(~33.36% F1)相比,我们所有的模型在这个新的多跨度数据集上的表现相似(21.492% F1)。虽然在源数据集上测试的模型稍微进行了微调,但性能足够相似,可以判断任务公式不会严重影响回答问题的能力。我们的评估表明,这些模型确实能够调整以回答需要多个答案的问题。我们希望我们的发现将有助于未来的问题回答的发展,并改进现有的问题回答产品和方法。
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Importance of the Single-Span Task Formulation to Extractive Question-answering
Recent progress in machine reading comprehension and question-answering has allowed machines to reach and even surpass human question-answering. However, the majority of these questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset (21.492% F1) compared to the single-span source datasets (~33.36% F1). While the models tested on the source datasets were slightly fine-tuned, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in questionanswering and improve existing question-answering products and methods.
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