Large Language Models (LLMs) demonstrate remarkable performance in machine translation. Recent studies indicate that for high-resource languages, LLM surpasses encoder-decoder neural machine translation (NMT) models. However, evaluation datasets used in many LLM-based translation studies are often compromised by data leakage and lack demanding datasets that accurately gauge the potential and limitations of LLMs in human-like translation. This paper introduces a manually constructed Korean-English discourse-level corpus comprising 600 text instances featuring six linguistic phenomena: lexical ambiguity, zero anaphora, slang, idiom, figurative language, and implicature. Utilizing this challenge test set, we investigated LLM’s Korean-to-English translation capability, particularly in cases requiring inter-sentential context based semantic inference. The findings reveal that state-of-the-art LLM, such as GPT-4o, still struggle with specific linguistic phenomena that can be challenging for machine translation. Additionally, step-by-step prompting, such as Chain-of-Thought (CoT) prompting, significantly enhance the translation performance of LLMs compared to zero-shot prompting.

Using current methods, the construction of multilingual resources in FrameNet is an expensive and complex task. While crowdsourcing is a viable alternative, it is difficult to include non-native English speakers in such efforts as they often have difficulty with English-based FrameNet tools. In this work, we investigated cross-lingual issues in crowdsourcing approaches for multilingual FrameNets, specifically in the context of the newly constructed Korean FrameNet. To accomplish this, we evaluated the effectiveness of various crowdsourcing settings whereby certain types of information are provided to workers, such as English definitions in FrameNet or translated definitions. We then evaluated whether the crowdsourced results accurately captured the meaning of frames both cross-culturally and cross-linguistically, and found that by allowing the crowd workers to make intuitive choices, they achieved a quality comparable to that of trained FrameNet experts (F1 > 0.75). The outcomes of this work are now publicly available as a new release of Korean FrameNet 1.1.

In this paper, for the purpose of enhancing Universal Dependencies for the Korean language, we propose a modified method for mapping Korean Part-of-Speech(POS) tagset in relation to Universal Part-of-Speech (UPOS) tagset in order to enhance the Universal Dependencies for the Korean Language. Previous studies suggest that UPOS reflects several issues that influence dependency annotation by using the POS of Korean predicates, particularly the distinctiveness in using verb, adjective, and copula.