论文标题
多文档阅读理解
Multi Document Reading Comprehension
论文作者
论文摘要
阅读理解（RC）是从给定段落或一组段落中回答问题的任务。在多个段落的情况下，任务是找到问题的最佳答案。自然语言处理领域（NLP）的最新试验和实验已经证明，可以提供机器的能力，不仅能够处理段落中的文本并了解其含义以回答段落中的问题，而且还可以超过许多数据集中的人类绩效，例如Standford的问题，例如Standford的问题回答DataSet（Squead）。本文介绍了过去几十年来关于阅读理解及其在自然语言处理中的发展的研究。我们还将研究单个文档阅读理解的任务如何充当我们的多文章阅读理解系统的基础。在本文的后半部分，我们将研究一个最近提出的用于多文档阅读理解的模型-RE3QA，由读取器，猎犬和基于重新级别的网络组成，以从给定的一组通道中获取最佳答案。
Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.
