119 datasets found

The WIKIQA dataset is used for evaluation of the proposed QMWF-LM approach.

The TREC-QA dataset is a benchmark dataset for question answering task.

The proposed QLM-EE model is used for question answering task on two benchmark datasets, TREC-QA and WIKIQA.

The TREC-COVID dataset is a collection of journal articles related to COVID-19 and other coronaviruses, with human annotators providing relevancy judgments at the end of each...

The dataset used in the paper is a set of sequential vision-and-language tasks, where each task consists of an image and a text input.

OVQA is a medical visual question answering dataset.

Slake is a medical visual question answering dataset.

Knowledge graphs (KGs) are collections of facts. Some well-known knowledge graphs include Freebase (Bollacker et al., 2008), Word-Net (Miller, 1995), YAGO (Suchanek et al.,...

Knowledge graphs store a wealth of knowledge from the real world into structured graphs, which consist of collections of triplets, and each triplet (h, r, t) represents that...

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 dataset used in the paper is also mentioned as CommonsenseQA, which is a 5-way multiple choice QA dataset that requires commonsense knowledge.

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 is used for training and evaluating the MS MARCO model, a question answering model.

Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the...

The dataset used for FAQ sentence labeling.

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there...

Visual dialog is a multi-round extension for VQA. The interactions between the image and multi-round question-answer pairs (history) are progressively changing, and the...

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation...

The paper discusses the use of multi-objective Bayesian optimization for hyperparameter transfer in topic models.