Contrastive positive sample propagation along the audio-visual event line
Visual and audio signals often coexist in natural environments, forming audio-visual events
(AVEs). Given a video, we aim to localize video segments containing an AVE and identify its …
(AVEs). Given a video, we aim to localize video segments containing an AVE and identify its …
Contrastive data and learning for natural language processing
Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …
learning is one such technique to learn an embedding space such that similar data sample …
Generative aspect-based sentiment analysis with contrastive learning and expressive structure
Generative models have demonstrated impressive results on Aspect-based Sentiment
Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category …
Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category …
Contrastive learning models for sentence representations
Sentence representation learning is a crucial task in natural language processing, as the
quality of learned representations directly influences downstream tasks, such as sentence …
quality of learned representations directly influences downstream tasks, such as sentence …
Learning label hierarchy with supervised contrastive learning
Supervised contrastive learning (SCL) frameworks treat each class as independent and thus
consider all classes to be equally important. This neglects the common scenario in which …
consider all classes to be equally important. This neglects the common scenario in which …
[HTML][HTML] Cantonese natural language processing in the transformers era: a survey and current challenges
Despite being spoken by a large population of speakers worldwide, Cantonese is under-
resourced in terms of the data scale and diversity compared to other major languages. This …
resourced in terms of the data scale and diversity compared to other major languages. This …
ACF: aligned contrastive finetuning for language and vision tasks
Contrastive learning (CL) has achieved great success in various fields with self-supervised
learning. However, CL under the supervised setting is not fully explored, especially how to …
learning. However, CL under the supervised setting is not fully explored, especially how to …
Crl+: A novel semi-supervised deep active contrastive representation learning-based text classification model for insurance data
AN Jahromi, E Pourjafari, H Karimipour… - arXiv preprint arXiv …, 2023 - arxiv.org
Financial sector and especially the insurance industry collect vast volumes of text on a daily
basis and through multiple channels (their agents, customer care centers, emails, social …
basis and through multiple channels (their agents, customer care centers, emails, social …
MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
The eXtreme Multi-label text Classification (XMC) refers to training a classifier that assigns a
text sample with relevant labels from an extremely large-scale label set (eg, millions of …
text sample with relevant labels from an extremely large-scale label set (eg, millions of …
[PDF][PDF] Data-Efficient Learning for Healthcare Queries In Low-Resource And Code Mixed Language Settings
S Mwongela, J Patel, S Rajasekharan… - International …, 2023 - pml4dc.github.io
The leading approaches in modern Natural Language Processing (NLP) are notoriously
data-hungry. A good example is Transformer models, which achieve surging and state-of …
data-hungry. A good example is Transformer models, which achieve surging and state-of …