NEU er at S em E val-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension (2021)

Abstract: SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware model. However, we argue that straightforward concatenation can only provide a coarse-grained context for the MRC task, ignoring the specific positions of the option relative to the question. In this paper, we propose a novel MRC model by filling options into the question to produce a fine-grained context (defined as summary) which can better reveal the relationship between option and question. We conduct a series of experiments on the given dataset, and the results show that our approach outperforms other counterparts to a large extent.

## introduction in order to make the computer understand, represent and express better, we study the ability of mrc to understand abstract definitions @xcite in the reading comprehension of abstract meaning task(recam). in recam task, there are two kinds of abstract definitions, one is imperceptibility and the other is nonspecificity. to evaluate the model performance comprehensively, three subtasks are designed @xcite : subtask 1) recam-imperceptibility -a passage, a question with a placeholder, and answers used to fill in the placeholder are given. the answers are all imperceptibility words so as to evaluatethe model's ability to comprehend these imperceptibility words. subtask 2) recam-nonspecificity -a passage, a question with a placeholder, and answers used to fill in the placeholder are given. the answers are all nonspecificity words so as to evaluatethe model's ability to comprehend these nonspecificity words. subtask 3) to provide more insights into the relationship between two abstract views, it requires to test the performance of the model trained on one data set and evaluated on the other one. because question is obtained by hollowing out summary of passage and answer is abstract words to fill in @xcite , there is a semantically complementary relationship between the question and answer. as shown in the figure 1 , in the previous work @xcite , the model inputs passage, question and answer independently. it can only provide a coarsegrained context for the mrc task, ignoring the specific positions of the option relative to the question. quesiton cannot be a complete sentence, and the answer lacks contextual information about the question. to solve this problem, we introduce complete summary representation by filling answers into question for maching reading @xcite . comsr convert the original question and answer into summary. in this way, we can turn the problem into matching correct summary for the passage. by replacing the answer option into the question placeholder, the original question has higher semantic integrity, and the original answer obtains the context information from the question to eliminate language ambiguity such as polysemy. based on the results, comsr is superior to pretrained language models and the latest mrqa models, with the accuracy rate of 64.76% in subtask 1, 64.86% in subtask 2, and shows great generalization ability in subtask 3. ## system overview figure 2 illustrates the overall structure of comsr. since just one summary option belongs to the passage, we input passage and summary together to better reflect the corresponding relationship between the passage and the summaries. as bert has been shown to be a powerful feature extractor for various tasks @xcite , we use bert as the encoder to extract the vector representation of the semantic meaning of the passage and the summaries, as well as the relationship between the passage and the summaries. finally, we use a classifier layer to convert the hidden state into the final selection of the model. ## conclusion we proposed comsr for multiple choice reading comprehension to complete summary representation. merging the separated question and answer into a summary could improve semantic integrity and better reveal the relationship between option and question. results showed that our model can achieve relatively high performance compared to the latest baseline in terms of accuracy and generalization ability.