The machine translation system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation. One factor that significantly affects the performance of NMT is the availability of high-quality parallel corpora. However, high-quality parallel corpora concerning Korean are relatively scarce compared to those associated with other high-resource languages, such as German or Italian. To address this problem, AI Hub recently released seven types of parallel corpora for Korean. In this study, we conduct an in-depth verification of the quality of corresponding parallel corpora through Linguistic Inquiry and Word Count (LIWC) and several relevant experiments. LIWC is a word-counting software program that can analyze corpora in multiple ways and extract linguistic features as a dictionary base. To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT. Our findings suggest the direction of further research toward obtaining the improved quality parallel corpora through our correlation analysis in LIWC and NMT performance.

Relation Extraction (RE) aims to predict the correct relation between two entities from the given sentence. To obtain the proper relation in Relation Extraction (RE), it is significant to comprehend the precise meaning of the two entities as well as the context of the sentence. In contrast to the RE research in English, Korean-based RE studies focusing on the entities and preserving Korean linguistic properties rarely exist. Therefore, we propose K-EPIC (Entity-Perceived Context representation in Korean) to ensure enhanced capability for understanding the meaning of entities along with considering linguistic characteristics in Korean. We present the experimental results on the BERT-Ko-RE and KLUE-RE datasets with four different types of K-EPIC methods, utilizing entity position tokens. To compare the ability of understanding entities and context of Korean pre-trained language models, we analyze HanBERT, KLUE-BERT, KoBERT, KorBERT, KoELECTRA, and multilingual-BERT (mBERT). The experimental results demonstrate that the F1 score increases significantly with our K-EPIC and that the performance of the language models trained with the Korean corpus outperforms the baseline.

Grounded Multimodal Named Entity Recognition (GMNER) aims to recognize textual entities and ground them to corresponding visual regions, requiring effective integration of heterogeneous multimodal information. However, existing approaches often rely on independent entity--region alignment and overlook structured inter-entity dependencies, leading to ambiguous grounding and redundant region assignments, particularly in multi-entity scenarios. To address these challenges, we propose ANCOR, a structured framework for GMNER based on anchor-guided grounding. ANCOR follows a three-stage design that progressively exploits complementary textual and visual information. First, a boundary-guided multimodal entity recognition stage produces structured entity predictions. Second, a type-aware comparative visual prior generation stage leverages fine-grained entity types to construct discriminative visual priors for grounding. Third, an anchor-guided grounding mechanism models structured inter-entity dependencies through anchor-guided exclusion during training, where a confidently grounded entity is selected as an anchor to guide the disambiguation of others. Extensive experiments on benchmark GMNER datasets demonstrate that ANCOR consistently outperforms existing methods, particularly in challenging multi-entity scenarios. These results demonstrate the effectiveness of explicitly modeling cross-entity dependencies for accurate and robust multimodal entity grounding.