论文标题
概率假设很重要：改进的遥远监督文档级别问题的模型回答
Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering
论文作者
论文摘要
我们使用文档级遥远的超级视频，配对问题和相关文档与答案字符串解决了提取问题答案的问题。我们比较先前使用的概率空间和遥远的超级视频假设（关于弱答案字符串标签和可能的答案之间的对应关系的假设）。我们表明这些假设相互作用，并且不同的配置提供了互补的好处。我们证明，多目标模型可以有效地结合多个假设的优势，并超越最佳的个人配方。我们的方法在triviaqa-wiki上的F1中的先前最新模型优于先前的最新模型，而在叙事中摘要中，Rouge-L中的最先前模型则优于1.7分。
We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previously used probability space and distant super-vision assumptions (assumptions on the correspondence between the weak answer string labels and possible answer mention spans). We show that these assumptions interact, and that different configurations provide complementary benefits. We demonstrate that a multi-objective model can efficiently combine the advantages of multiple assumptions and out-perform the best individual formulation. Our approach outperforms previous state-of-the-art models by 4.3 points in F1 on TriviaQA-Wiki and 1.7 points in Rouge-L on NarrativeQA summaries.
