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.
170 PAPERS • 1 BENCHMARK
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.
152 PAPERS • 1 BENCHMARK
Multilingual Knowledge Questions and Answers (MKQA) is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Answers are based on a language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering.
37 PAPERS • NO BENCHMARKS YET
Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
19 PAPERS • NO BENCHMARKS YET
license: apache-2.0 tags: human-feedback size_categories: 100K<n<1M pretty_name: OpenAssistant Conversations
14 PAPERS • NO BENCHMARKS YET
MINTAKA is a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. It is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers.
10 PAPERS • NO BENCHMARKS YET
RELX is a benchmark dataset for cross-lingual relation classification in English, French, German, Spanish and Turkish.
6 PAPERS • NO BENCHMARKS YET
XQA is a data which consists of a total amount of 90k question-answer pairs in nine languages for cross-lingual open-domain question answering.
GermanQuAD is a Question Answering (QA) dataset of 13,722 extractive question/answer pairs in German.
5 PAPERS • NO BENCHMARKS YET
X-WikiRE is a new, large-scale multilingual relation extraction dataset in which relation extraction is framed as a problem of reading comprehension to allow for generalization to unseen relations.
2 PAPERS • NO BENCHMARKS YET
Contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph with more than 970 thousand events.
1 PAPER • NO BENCHMARKS YET
Multitask learning has led to significant advances in Natural Language Processing, including the decaNLP benchmark where question answering is used to frame 10 natural language understanding tasks in a single model. PQ-decaNLP is a crowd-sourced corpus of paraphrased questions, annotated with paraphrase phenomena. This enables analysis of how transformations such as swapping the class labels and changing the sentence modality lead to a large performance degradation.
QALD-9-Plus Dataset Description QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.
1 PAPER • 1 BENCHMARK