2021/05/28 by Arnab Debnath, Debnath, Arnab, Navid Rajabi +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2105.14115
openalex publication_date 2021/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Question answering (QA) in English has been widely explored, but multilingual\ndatasets are relatively new, with several methods attempting to bridge the gap\nbetween high- and low-resourced languages using data augmentation through\ntranslation and cross-lingual transfer. In this project, we take a step back\nand study which approaches allow us to take the most advantage of existing\nresources in order to produce QA systems in many languages. Specifically, we\nperform extensive analysis to measure the efficacy of few-shot approaches\naugmented with automatic translations and permutations of\ncontext-question-answer pairs. In addition, we make suggestions for future\ndataset development efforts that make better use of a fixed annotation budget,\nwith a goal of increasing the language coverage of QA datasets and systems.\nCode and data for reproducing our experiments are available here:\nhttps://github.com/NavidRajabi/EMQA.\n