[HTML][HTML] Deep Learning applications for COVID-19
This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …
directions for future research on COVID-19. We cover Deep Learning applications in Natural …
[HTML][HTML] A survey on deep learning for textual emotion analysis in social networks
S Peng, L Cao, Y Zhou, Z Ouyang, A Yang, X Li… - Digital Communications …, 2022 - Elsevier
Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …
The rise and potential of large language model based agents: A survey
For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
Simcse: Simple contrastive learning of sentence embeddings
This paper presents SimCSE, a simple contrastive learning framework that greatly advances
state-of-the-art sentence embeddings. We first describe an unsupervised approach, which …
state-of-the-art sentence embeddings. We first describe an unsupervised approach, which …
Consert: A contrastive framework for self-supervised sentence representation transfer
Learning high-quality sentence representations benefits a wide range of natural language
processing tasks. Though BERT-based pre-trained language models achieve high …
processing tasks. Though BERT-based pre-trained language models achieve high …
An introduction to deep learning in natural language processing: Models, techniques, and tools
Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …
involves the design and implementation of systems and algorithms able to interact through …
DiffCSE: Difference-based contrastive learning for sentence embeddings
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence
embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference …
embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference …
[PDF][PDF] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
N Reimers - arXiv preprint arXiv:1908.10084, 2019 - fq.pkwyx.com
BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art
performance on sentence-pair regression tasks like semantic textual similarity (STS) …
performance on sentence-pair regression tasks like semantic textual similarity (STS) …
Parameter-efficient transfer learning with diff pruning
While task-specific finetuning of pretrained networks has led to significant empirical
advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-task …
advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-task …
Declutr: Deep contrastive learning for unsupervised textual representations
Sentence embeddings are an important component of many natural language processing
(NLP) systems. Like word embeddings, sentence embeddings are typically learned on large …
(NLP) systems. Like word embeddings, sentence embeddings are typically learned on large …