XQuAD Dataset Papers With Code

XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages.

Machine Learning Datasets

XQuAD Benchmark (Cross-Lingual Question Answering)

iFLYTEK & HIT Reading Comprehension Model Betters Humans, Tops

LSP Dataset - Machine Learning Datasets

Papers with code or without code? Impact of GitHub repository

NukeBERT: A Pre-trained language model for Low Resource Nuclear

SberQuAD Dataset Papers With Code

CovidQA Dataset Papers With Code

UQuAD1.0: Development of an Urdu Question Answering Training Data

An example from the SQuAD dataset. Evidences needed for the answer

Papers with Code

P] Browse State-of-the-Art Papers with Code : r/MachineLearning

XQA: A Cross-lingual Open-domain Question Answering Dataset

How to finetune UnifiedQA T5 for Causal SQUAD Question Answering