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Found 8 papers, 3 papers with code
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.
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data
Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems.
Ranked #1 on
Question Answering
on HybridQA
A Versatile Framework for Evaluating Ranked Lists in terms of Group Fairness and Relevance
We present a simple and versatile framework for evaluating ranked lists in terms of group fairness and relevance, where the groups (i. e., possible attribute values) can be either nominal or ordinal in nature.
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-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.
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
