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Found 9 papers, 3 papers with code
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
Self-Distilled Self-Supervised Representation Learning
Through self-distillation, the intermediate layers are better suited for instance discrimination, making the performance of an early-exited sub-network not much degraded from that of the full network.
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers.
Ranked #10 on
Natural Language Inference
on SNLI
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA
In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the main stage as an auxiliary learning.
Korean Language Modeling via Syntactic Guide
Our experiment results prove that the proposed methods improve the model performance of the investigated Korean language understanding tasks.
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification
This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods.
Unifying Vision-Language Representation Space with Single-tower Transformer
Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations.
