基于多层次信息融合的多跳机器阅读理解

以往机器阅读理解模型中存在文本特征提取单一, 文本和问题的交互信息不全面等问题, 导致模型不能充分对文本进行理解, 本文提出了一种多层次信息融合的机器阅读理解模型. 通过在不同位置使用不同方法, 对文本信息进行多种层次的获取. 使用膨胀卷积网络捕捉文本的全局信息, 采用双向注意力机制和自注意力机制融合文本和问题之间的交互信息, 通过指针网络预测答案及其对应的支撑句. 该模型在CAIL2019和CAIL2020阅读理解数据集上训练的联合F1值分别达到50.09%和58.44%, 相比于其他基线模型取得了明显的性能提升.

Multi-hop Machine Reading Comprehension Based on Multi-level Information Fusion

In previous machine reading comprehension models, there were some problems, such as single-text feature extraction and incomplete interactive information between text and questions, which led to insufficient text understanding. This study proposes a machine reading understanding model with multi-level information fusion, which can obtain text information at multiple levels by using different methods in different locations. The model uses the dilated convolutional network to capture the global information of the text. Bi-directional attention mechanism and self-attention mechanism are used to fuse the interactive information between text and questions. Finally, the answer and its corresponding supporting sentence are predicted through the pointer network. The joint F1 values of the model trained on the CAIL2019 and CAIL2020 reading comprehension datasets reach 50.09% and 58.44% respectively, which achieves significant performance improvement compared with other baseline models.