MGRE: A Multi-Granularity Representation Enhancement Method in Multiple-Choice Question Answering
- Resource Type
- Conference
- Authors
- Hou, Yuhang; Liu, Xiyan; Song, Jiaxing; Shi, Yidong; Liu, Ruifang
- Source
- 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2023 19th International Conference on. :1-5 Jul, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Knowledge discovery
Question answering (information retrieval)
Cognition
Task analysis
Fuzzy systems
Multiple-Choice Question Answering
Machine Reading Comprehension
Multi-Granularity Representation Enhancement
- Language
Multiple-choice question answering(MCQA) is one of the most challenging tasks in machine reading comprehension. MCQA task requires selecting the most appropriate answer from several relevant options for a given question. In recent years, many works have concentrated on designing models from the perspective of using the information of the question and options at a large granularity level. However, few studies have explored how the model uses the information to find the correct answer at a fine granularity level or a multi-granularity level. This paper proposed a multi-granularity representation enhancement method to use information from different granularities. The method introduces large-grained candidate option information into the question to guide the selection of fine-grained critical information and facilitate the information interaction between the answer and the question which is in line with the human reasoning processes. Experimental results show that the method proposed in this paper can effectively improve the accuracy of MCQA tasks without introducing external knowledge.