420 datasets found

A dataset for neural keyphrase generation.

A dataset for neural keyphrase generation with layer-wise coverage attention.

A comprehensive evaluation framework for keyphrase systems, including reference agreement, faithfulness, diversity, and utility.

We propose DisCo-CLIP, a distributed memory-efficient CLIP training approach, to reduce the memory consump- tion of contrastive loss when training contrastive learning models.

A dataset consisting of ten years of customer service calls to a fleet truck company.

The Ubuntu Dialogue Corpus is the largest freely available multi-turn based dialogue corpus which consists of almost one million two-way conversations extracted from the Ubuntu...

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.

The CLIP model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of millions of image-text pairs can be...

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...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a condensed representation of LLM activations obtained from sparse...

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...

Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data.

This dataset is used for training and evaluating the proposed SynGEC approach.

This dataset is used for training and evaluating the proposed SynGEC approach.

This dataset is used for training and evaluating the proposed SynGEC approach.

The Penn Treebank (PTB) dataset is used for word ordering task. The dataset is used to evaluate the performance of different models for word ordering.

Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation.

PAUSE is a generic and end-to-end sentence embedding approach that exploits the labels and explores the unlabeled sentence pairs simultaneously.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...