all AI news
Unsupervised multiple choices question answering via universal corpus
Feb. 28, 2024, 5:49 a.m. | Qin Zhang, Hao Ge, Xiaojun Chen, Meng Fang
cs.CL updates on arXiv.org arxiv.org
Abstract: Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from the universal domain without relying on any form of manual annotation. Possible answers are extracted and used to produce related questions, then …
abstract annotated data arxiv building cs.cl data domain framework generate multiple novel paper question question answering scale study synthetic type universal unsupervised via
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Data Scientist
@ ITE Management | New York City, United States