MAUD is an expert-annotated merger agreement reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points study, where lawyers and law students answered 92 questions about 152 merger agreements. With over 39,000 examples and 47,000 total annotations, it is the largest expert-annotated legal reading comprehension dataset in the English language, as well as the first expert-annotated merger agreement dataset.

Large scale machine reading comprehension dataset in Urdu language.

A newly developed public dataset and the task of multiple property extraction. It uses the same data as WikiReading but does not inherit its predecessor's identified disadvantages. Source: From Dataset Recycling to Multi-Property Extraction and Beyond

IDK-MRC is an Indonesian Machine Reading Comprehension (MRC) dataset consists of more than 10K questions in total with over 5K unanswerable questions with diverse question types. Source: IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension Image Source: https://arxiv.org/pdf/2210.13778v1.pdf

NEREL-BIO is an annotation scheme and corpus of PubMed abstracts in Russian and English. It contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO) and cross-language (English -> Russian) transfer. Source: NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities Image Source: https://arxiv.org/pdf/2210.11913v1.pdf

PersianQA: a dataset for Persian Question Answering Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced the dataset consists of more than 9,000 entries. Each entry can be either an impossible-to-answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer". Moreover, the dataset has 900 test data available. On top of that, the very first models trained on the dataset, Transformers, are available online. All the crowd workers of the dataset are native Persian speakers. Also, it worth mentioning that the contexts are collected from all categories of the Wiki (Historical, Religious, Geography, Science, etc).

A high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. Source: QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications