Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain
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.- Anthology ID:
- 2021.ccl-1.88
- Volume:
- Proceedings of the 20th Chinese National Conference on Computational Linguistics
- Month:
- August
- Year:
- 2021
- Address:
- Huhhot, China
- Editors:
- Sheng Li (李生), Maosong Sun (孙茂松), Yang Liu (刘洋), Hua Wu (吴华), Kang Liu (刘康), Wanxiang Che (车万翔), Shizhu He (何世柱), Gaoqi Rao (饶高琦)
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 988–999
- Language:
- English
- URL:
- https://aclanthology.org/2021.ccl-1.88
- DOI:
- Cite (ACL):
- Tian Zhixing, Zhang Yuanzhe, Liu Kang, and Zhao Jun. 2021. Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 988–999, Huhhot, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain (Zhixing et al., CCL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.ccl-1.88.pdf
- Data
- MCTest, TweetQA