Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for
natural language tasks. Specifically, we first transfer a pre-trained model into a model for a …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Despite the impressive improvements achieved by unsupervised deep neural networks in
computer vision and NLP tasks, such improvements have not yet been observed in ranking …

Sentence matching is widely used in various natural language tasks such as natural
language inference, paraphrase identification, and question answering. For these tasks …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …