A Frame-based Sentence Representation for Machine Reading Comprehension (2020)

Abstract: Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

## introduction machine reading comprehension (mrc) requires machines to read and understand a text passage, and answer relevant questions about it. human beings can easily understand the meaning of a sentence based on their semantic knowledge. for instance, given a sentence katie bought some chocolate cookies, people know katie is a buyer, chocolate cookies are goods and belong to food class etc. existing machine learning approaches, however, face great challenges to address complicated mrc questions, as they do not have above semantic knowledge. nevertheless, framenet @xcite , as a knowledge base, provides schematic scenario representation that could be potentially leveraged to better understand sentences. ## frame representation model in this section, we present our frame representation model, considering both lus and f-to-f. let @xmath0, f 2 , . . . , f m , . . .} represents a set of all frames in framenet, where f @xmath1. let u @xmath2, u fm 2 , . . . , u fm n , . . .} be the lus set of f m , where u @xmath3, n stands for the total number of lus in f m , and u fm n be the n-th lu of f m . t fm is a target word, matching a lu in f m . we proposed 3 different frame representation models. ## frame-based sentence representation given a sentence @xmath4, x 2 , . . . , x k , . . . } where each x k is a word, let t k be the k-th frame-evoking target of s, and t k evokes f k frame. f e ki denotes the i-th frame element of f k , and p ki denotes the ith span fulfilling f e ki . we define a frame semantic quadruple c @xmath5, f k , f e kn , p kn >, where c k represents the k-th quadruple of s. ## conclusion we propose a novel frame-based sentence representation method, which integrates multi-frame semantic information to facilitate sentence modelling. our extensive experimental results demonstrate it works very well for the challenging machine reading comprehension task.