论文标题
阅读理解为自然语言推论：语义分析
Reading Comprehension as Natural Language Inference: A Semantic Analysis
论文作者
论文摘要
在最近的过去，自然语言推论（NLI）引起了人们的重大关注，尤其是鉴于其对下游NLP任务的希望。但是，其真正的影响是有限的，尚未得到很好的研究。因此，在本文中，我们探讨了NLI对最突出的下游任务之一的实用性，即。问答（QA）。我们将最大的MRC数据集（Race）之一转换为NLI形式，并比较了两种形式的最先进模型（Roberta）的性能。我们提出了有关问题的新特征，并评估了质量检查和NLI模型在这些类别上的性能。我们重点介绍了当数据以连贯的需要形式和结构化的提问串联形式显示时，模型能够更好地执行的明确类别。
In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.
