Exploring Expected Answer Types for Effective Question Answering Systems for low resource language
Authors: CHINDUKURI MALLIKARJUNA, Sangeetha Sivanesan


Reviewer paper summaries: The paper presents an approach to improve the accuracy of QA systems for Dravidian languages by appending additional linguistic features (answer-types) into the input of pre-trained language models. The experiments are conducted on the translated Tamil QA dataset and Chaii dataset, and the results demonstrate significant improvements with Expected Answer Types (EAT) features compared to those without EAT.
Summary of strengths: The paper contributes to the knowledge of low-resource QA systems for Dravidian languages by incorporating additional linguistic features into pre-trained language models, showing that adding such features could improve the accuracy of QA systems.
Summary of weaknesses: However, such contribution is limited, and the methodology proposed in this paper is not innovative. Specifically, incorporating additional features into the input of language models to enhance the understanding of specific tasks was commonly used in low-resource scenarios. In addition, the experimental analysis is limited. Further detailed analysis should be included in the discussion section. Finally, how to use modern LLMs to do this task would be a good direction in future work.
Comments, suggestions and typos: No
Overall assessment scores: 1