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. Source: XQuAD Image Source: https://arxiv.org/pdf/1910.11856v3.pdf

MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. Source: Facebook Research Image Source: https://github.com/facebookresearch/mlqa

TyDiQA is the gold passage version of the Typologically Diverse Question Answering (TyDiWA) dataset, a benchmark for information-seeking question answering, which covers nine languages. The gold passage version is a simplified version of the primary task, which uses only the gold passage as context and excludes unanswerable questions. It is thus similar to XQuAD and MLQA, while being more challenging as questions have been written without seeing the answers, leading to 3× and 2× less lexical overlap compared to XQuAD and MLQA respectively. Source: XTREME

QALD-9-Plus Dataset Description QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9. QALD-9-Plus enables to train and test KGQA systems over DBpedia and Wikidata using questions in 9 different languages: English, German, Russian, French, Armenian, Belarusian, Lithuanian, Bashkir, and Ukrainian. Some of the questions have several alternative writings in particular languages which enables to evaluate the robustness of KGQA systems and train paraphrasing models. As the questions' translations were provided by native speakers, they are considered as "gold standard", therefore, machine translation tools can be trained and evaluated on the dataset. # Dataset Statistics | | en | de | fr | ru | uk | lt | be | ba | hy | # questions DBpedia | # questions Wikidata | |-------|:---:|:---:|:--:|:----:|:---:|:---:|:---:|:---:|:--:|:-----------:|:-----------:| | Train | 408 | 543 | 260 | 1203 | 447 | 468 | 441 | 284 | 80 | 408 | 371 | | Test | 150 | 176 | 26 | 348 | 176 | 186 | 155 | 117 | 20 | 150 | 136 | Given the numbers, it is obvious that some of the languages are covered more than once i.e., there is more than one translation for a particular question. For example, there are 1203 Russian translations available while only 408 unique questions exist in the training subset (i.e., 2.9 Russian translations per one question). The availability of such parallel corpora enables the researchers, developers and other dataset users to address the paraphrasing task. [cc-by]: http://creativecommons.org/licenses/by/4.0/ [cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg

An open-source online generative dictionary that takes a word and context containing the word as input and automatically generates a definition as output. Incorporating state-of-the-art definition generation models, it supports not only Chinese and English, but also Chinese-English cross-lingual queries. Moreover, it has a user-friendly front-end design that can help users understand the query words quickly and easily.