一种基于数据重构和富特征的神经网络机器阅读理解模型

该文描述了ZWYC团队在“2018机器阅读理解技术竞赛”上提出的机器理解模型。所提出模型将机器阅读理解问题建模成连续文本片段抽取问题,提出基于富语义特征的神经交互网络模型。为了充分使用答案标注信息,模型首先对数据进行细致的重构,让人工标注的多个答案信息都能融合到数据中。通过特征工程,对每个词构建富语义表征。同时提出一种简单有效的问题和文档交互的方式,得到问题感知的文档表征。基于多个文档串接的全局表征,模型进行答案文本预测。在最终测试集上,该模型获得了目前先进的结果,在105支队伍中排名第2。

A Neural Machine Reading Comprehension Model Based on Relabeling and Rich Features

This paper describes the model proposed in “2018 NLP Challenge on Machine Reading Comprehension” by ZWYC team. Treated the machine reading comprehension as extracting the text span from the documents, this paper proposes a feature-rich neural interaction network. In order to effectively use the information of golden answers, our model first reconstructs the data in detail so that all golden answer information can be integrated. Then a feature-rich semantic representation is built for each word. Moreover, a simple but effective network is designed for question-aware representation for each document by captuing the interaction between questions and documents. The proposed model predicts answer text based on global representations of multiple candidate documents, leading to the runner-up position among 105 teams.