Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain (2021)

Abstract: In this paper we focus on machine reading comprehension in social media. In this domain onenormally posts a message on the assumption that the readers have specific background knowledge. Therefore those messages are usually short and lacking in background information whichis different from the text in the other domain. Thus it is difficult for a machine to understandthe messages comprehensively. Fortunately a key nature of social media is clustering. A group of people tend to express their opinion or report news around one topic. Having realized this we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages. The experiments on TweetQA datasets demonstrate the effectiveness of our method.

## introduction as an increasing number of people share and obtain information from social media, social media is now becoming an important real-time information source. the unprecedented volume, variety of usergenerated content, and the user interaction network constitute new opportunities for understanding social behavior and building socially intelligent systems. it is important and challenging to teach a machine to automatically understand the content presented in social media. although considerable progress has been made in the task of machine reading comprehension (mrc), most of the previous works only focus on the comprehension of the other domains, such as news @xcite , story @xcite and wikipedia @xcite , and there are very few works addressing the problem of social media mrc. message: in case you missed it, here's your first look at jamie bell's the thing: # fantasticfour empire magazine (@ empiremagazine) april @xcite question: what movie is being shared here? answer: fantastic four to obtain background knowledge for a machine reader, one feasible method is to introduce external knowledge from the knowledge base like conceptnet @xcite and wordnet @xcite , as the previous works do in other domains @xcite @xcite . unfortunately, due to the nature of informality and diversity of the messages in social media, some key phrases of the short messages cannot be found in those pre-constructed knowledge base. for example, in ## conclusion in this paper, we focus on machine reading comprehension in social media domain. we propose a novel method to address the problem of lacking in background knowledge in this task. utilizing the nature of clustering of social media, we retrieve and refine topic knowledge from the relevant messages, and then integrate the knowledge into an mrc model, tkr. experimental results show that our proposed method outperforms the recently proposed models and the bert-based baselines, which proves the method effective overall. by introducing different amount of topic knowledge, we demonstrate the effectiveness of our refined knowledge. moreover, the ablation study further validates the contribution of the key modules of tkr for utilizing the knowledge.