20 datasets found

The ATIS2 and ATIS3 datasets are used to create low-latency natural language understanding components.

The General Language Understanding Evaluation (GLUE) dataset is a dataset used in the paper to evaluate the performance of natural language understanding models.

The FewCLUE dataset is a Chinese few-shot learning evaluation benchmark.

WALNUT is a benchmark for semi-weakly supervised learning for natural language understanding. It consists of 8 NLU tasks with different types, including document-level and...

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

The MASSIVE dataset is a comprehensive collection of approximately one million annotated utterances for various natural language understanding tasks such as slot-filling, intent...

The IPA dataset contains a set of Chinese utterances that were collected and annotated in the development process of a commercialized Intelligent Personal Assistant (IPA) named...

The OSQ dataset covers 150 IND intents and also provides a set of manually labeled Out-of-Scope Queries (OSQ) that are not supported by the current system.

TreeMix is a compositional data augmentation approach for natural language understanding. It leverages constituency parsing tree to decompose sentences into sub-structures and...

The CoLA dataset has 8551 train and 527 development in domain samples.

A corpus and cloze evaluation for deeper understanding of commonsense stories.

The ROCStories corpus is a collection of crowdsourced five-sentence everyday stories rich in causal and temporal relations.

A corpus and cloze evaluation for deeper understanding of commonsense stories.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used three downstream tasks from the GLUE benchmark: Stanford Sentiment Treebank...

The Natural Instructions (NI) dataset used for evaluating the performance of the DEPTH model on natural language understanding tasks.

The Bing dataset is a large-scale dataset for natural language understanding and question answering.

The MS MARCO dataset is a large-scale dataset for natural language understanding and question answering.

The dataset used in the paper is the Stanford Natural Language Inference (SNLI) dataset, which consists of 549,367 premise-hypothesis pairs for train/dev/test sets and target...

This paper proposes BERT, a pre-trained deep bidirectional transformer for language understanding.

Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have...