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Found 30 papers, 17 papers with code
Integrating Transformers and Knowledge Graphs for Twitter Stance Detection
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Deep quantization methods have shown high efficiency on large-scale image retrieval.
To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering
Medical open-domain question answering demands substantial access to specialized knowledge.
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion
State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE).
CLEX: Continuous Length Extrapolation for Large Language Models
We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths.
GenKIE: Robust Generative Multimodal Document Key Information Extraction
Key information extraction (KIE) from scanned documents has gained increasing attention because of its applications in various domains.
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models
Our experiments validate the effectiveness of SparklesChat in understanding and reasoning across multiple images and dialogue turns.
When hard negative sampling meets supervised contrastive learning
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss.
Label Denoising through Cross-Model Agreement
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
BAND: Biomedical Alert News Dataset
Infectious disease outbreaks continue to pose a significant threat to human health and well-being.
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization
To alleviate the need of human effort, dictionary-based approaches have been proposed to extract named entities simply based on a given dictionary.
LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers
Vision Transformers (ViTs) have emerged as popular models in computer vision, demonstrating state-of-the-art performance across various tasks.
COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word.
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks.
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective.
Can Pretrained Language Models (Yet) Reason Deductively?
Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks.
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs).
Variational Continual Bayesian Meta-Learning
Conventional meta-learning considers a set of tasks from a stationary distribution.
Structure-Aware Random Fourier Kernel for Graphs
Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models
To catalyse the research in this direction, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, which is constructed based on the Unified Medical Language System (UMLS) Metathesaurus.
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks.
Few-Shot Table-to-Text Generation with Prototype Memory
Neural table-to-text generation models have achieved remarkable progress on an array of tasks.
Graph Neural Pre-training for Enhancing Recommendations using Side Information
For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks.
Learning Robust Recommenders through Cross-Model Agreement
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
Self-Alignment Pretraining for Biomedical Entity Representations
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems.
Semi-supervisedly Co-embedding Attributed Networks
Deep generative models (DGMs) have achieved remarkable advances.
Variational Bayesian Context-aware Representation for Grocery Recommendation
We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour.
Neural Variational Hybrid Collaborative Filtering
Specifically, we consider both the generative processes of users and items, and the prior of latent factors of users and items to be side informationspecific, which enables our model to alleviate matrix sparsity and learn better latent representations of users and items.
