12 citations · 40 across the 12 of their papers we have counts for
12 papers
Pre-training and Diagnosing Knowledge Base Completion Models
Vid Kocijan, Myeongjun Erik Jang, Thomas Lukasiewicz
In this work, we introduce and analyze an approach to knowledge transfer from one collection of facts to another without the need for entity or relation matching. The method works…
Improving Language Models Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary
Myeongjun Erik Jang, Thomas Lukasiewicz
The non-humanlike behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon of such faulty behaviours…
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley +2
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited r…
Consistency Analysis of ChatGPT
Myeongjun Erik Jang, Thomas Lukasiewicz
ChatGPT has gained a huge popularity since its introduction. Its positive aspects have been reported through many media platforms, and some analyses even showed that ChatGPT achiev…
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
Myeongjun Jang, Frank Mtumbuka, Thomas Lukasiewicz
The logical negation property (LNP), which implies generating different predictions for semantically opposite inputs, is an important property that a trustworthy language model mus…
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…
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Myeongjun Jang, Seungwan Seo, Pilsung Kang
Sequence-to-sequence (Seq2seq) models have played an important role in the recent success of various natural language processing methods, such as machine translation, text summariz…