7 citations · 17 across the 5 of their papers we have counts for
7 papers
KOBEST: Korean Balanced Evaluation of Significant Tasks
Dohyeong Kim, Myeongjun Jang, Deuk Sin Kwon +1
A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field, as it allows objective and precise evaluation of diverse…
Are Training Resources Insufficient? Predict First Then Explain!
Myeongjun Jang, Thomas Lukasiewicz
Natural language free-text explanation generation is an efficient approach to train explainable language processing models for commonsense-knowledge-requiring tasks. The most predo…
NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models
Myeongjun Jang, Thomas Lukasiewicz
The recent development in pretrained language models trained in a self-supervised fashion, such as BERT, is driving rapid progress in the field of NLP. However, their brilliant per…
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models
Myeongjun Jang, Deuk Sin Kwon, Thomas Lukasiewicz
Consistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding…
Sentence transition matrix: An efficient approach that preserves sentence semantics
Myeongjun Jang, Pilsung Kang
Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a se…
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Myeongjun Jang, Pilsung Kang
Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a…