Commonsense Reading Comprehension (CRC) is a significantly challenging task, aiming at choosing the right answer for the question referring to a narrative passage, which may require commonsense knowledge inference. Most of the existing approaches only fuse the interaction information of choice, passage, and question in a simple combination manner from a perspective, which lacks the comparison information on a deeper level. Instead, we propose a Multi-Perspective Fusion Network (MPFN), extending the single fusion method with multiple perspectives by introducing the and fusion along with the . More comprehensive and accurate information can be captured through the three types of fusion. We design several groups of experiments on MCScript dataset Ostermann:LREC18:MCScript to evaluate the effectiveness of the three types of fusion respectively. From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion. The experimental result also shows that our MPFN model achieves the state-of-the-art with an accuracy of 83.52 on the official test set.
This paper presents a new MRC model that is capable of three key comprehension skills: 1) handling rich variations in question types; 2) understanding potential answer choices; and 3) drawing inference through multiple sentences. The model is based on the proposed MUlti-Strategy Inference for Comprehension (MUSIC) architecture, which is able to dynamically apply different attention strategies to different types of questions on the fly. By incorporating a multi-step inference engine analogous to ReasoNet (, 2017), MUSIC can also effectively perform multi-sentence inference in generating answers. Evaluation on the RACE dataset shows that the proposed method significantly outperforms previous state-of-the-art models by 7.5 in relative accuracy.