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.

ACL Best Paper: Tricky Stanford DataSet Adds Questions That Don't

Understanding Semantic Search— (Part 1: Introduction to Machine

LSP Dataset - Machine Learning Datasets

MLQA Dataset Papers With Code

GitHub - pallavrajsahoo/Question-Answering-System-with-SQuAD

PDF] CodeQA: A Question Answering Dataset for Source Code

Dmitry Kobak on X: Really cool work by Pierre Lambert et al

Sensitivity to parameter choices on the Kazer et al.⁶ dataset and

PDF] VulBERTa: Simplified Source Code Pre-Training for

End to End Question-Answering System Using NLP and SQuAD Dataset

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

Papers With Code Machine Learning Papers and Code Free Resource

The Quick Guide to SQuAD. All the basic information you need to

Mapping global dynamics of benchmark creation and saturation in