Search Results for author:
Found 5 papers, 1 papers with code
Pragmatic Competence Evaluation of Large Language Models for Korean
The current evaluation of Large Language Models (LLMs) predominantly relies on benchmarks focusing on their embedded knowledge by testing through multiple-choice questions (MCQs), a format inherently suited for automated evaluation.
Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean
Almost all frameworks for the manual or automatic evaluation of machine translation characterize the quality of an MT output with a single number.
German Phoneme Recognition with Text-to-Phoneme Data Augmentation
In this study, we experimented to examine the effect of adding the most frequent n phoneme bigrams to the basic vocabulary on the German phoneme recognition model using the text-to-phoneme data augmentation strategy.
Grammar Accuracy Evaluation (GAE): Quantifiable Quantitative Evaluation of Machine Translation Models
Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language.
Korean-English Machine Translation with Multiple Tokenization Strategy
This work was conducted to find out how tokenization methods affect the training results of machine translation models.
