16 datasets found

The authors used a variety of datasets for question answering, including TriviaQA, Natural Questions, CountryQA, and Jeopardy questions.

A large annotated corpus for learning natural language inference

The dataset consists of approximately 10 million question-answer pairs from multiple languages covering diverse fields such as math and language, and strong variation in...

The SQuAD dataset is a benchmark for natural language understanding tasks, including question answering and text classification.

The Room-to-Room (R2R) dataset is a benchmark for vision-and-language navigation tasks. It consists of 7,189 paths sampled from its navigation graphs, each with three...

The TruthfulQA dataset is a dataset that contains 817 questions designed to evaluate language models' preference to mimic some human falsehoods.

The Quora Question Pairs (QQP) dataset consists of 50,000 question pairs labeled with paraphrase or non-paraphrase.

A general language assistant for aligning language models with human users

DuReader dataset is a Chinese machine reading comprehension dataset, focusing on real-world web data

The dataset used in the paper is a multiple-choice reading comprehension dataset, which includes a passage, question, and answer. The passage is a script, and the question is a...

The Natural Questions dataset consists of questions extracted from web queries, with each question accompanied by a corresponding Wikipedia article containing the answer.

The TriviaQA dataset is a collection of questions sourced from Quiz League websites, with sentence-level supporting facts annotation.

The dataset used for the experiments across ten models– ranging from bag-of-words models to pre-trained transformers– and find that a model having higher AUC does not necessarily...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

Seq2SQL: Generating structured queries from natural language using reinforcement learning.

Semantic parsing maps a user-issued natural language (NL) utterance to a machine-executable meaning representation (MR), such as λ−calculus (Zettlemoyer and Collins, 2005), SQL...