Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines' ability to understand human language.Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at the prediction.One of the most important interpretation forms is to attribute model decisions to input features.Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiplechoice MRC and improve the model by punishing the illogical attributions.Our method can improve model performance without any external information and model structure change.Furthermore, we also analyze how and why such a self-training method works.

This paper focuses on the answer sentence selection task. Unlike previous work, which only models the relation between the question and each candidate sentence, we propose Multi-Perspective Graph Encoder (MPGE) to take the relations among the candidate sentences into account and capture the relations from multiple perspectives. By utilizing MPGE as a module, we construct two answer sentence selection models which are based on traditional representation and pre-trained representation, respectively. We conduct extensive experiments on two datasets, WikiQA and SQuAD. The results show that the proposed MPGE is effective for both types of representation. Moreover, the overall performance of our proposed model surpasses the state-of-the-art on both datasets. Additionally, we further validate the robustness of our method by the adversarial examples of AddSent and AddOneSent.

This paper focuses on machine reading comprehension for narrative passages.Narrative passages usually describe a chain of events.When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively.Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.Specifically, we build a scene graph by utilizing Atomic as the external knowledge and propose a novel Graph Dimensional-Iteration Network (GDIN) to encode the graph.We conduct experiments on the ROCStories, a dataset of Story Cloze Test (SCT), and Cos-mosQA, a dataset of multiple choice.Our method achieves state-of-the-art.

Open-domain question answering focuses on using diverse information resources to answer any types of question. Recent years, with the development of large-scale data set and various deep neural networks models, some recent advances in open domain question answering system first utilize the distantly supervised dataset as the knowledge resource, then apply deep learning based machine comprehension techniques to generate the right answers, which achieves impressive results compared with traditional feature-based pipeline methods.