Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh, Chaean Kang, Joeun Kang, Xiaonan Wang, Hyunjung Park, Young Cheol Jung, Hansaem Kim. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Figurative language conveys stance, emotion, and social nuance, making its appropriate use essential in dialogue.While large language models (LLMs) often succeed in recognizing figurative expressions at the sentence level, their ability to use them coherently in conversation remains uncertain.We introduce FLUID QA, the first multilingual benchmark that evaluates figurative usage in dialogue across English, Korean, and Chinese.Each item embeds figurative choices into multi-turn contexts.To support interpretation, we include FLUTE-bi, a sentence-level diagnostic task.Results reveal a persistent gap: models that perform well on FLUTE-bi frequently fail on FLUID QA, especially in sarcasm and metaphor.These errors reflect systematic rhetorical confusion and limited discourse reasoning.FLUID QA provides a scalable framework for assessing usage-level figurative competence across languages.

Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts.Yet, a systematic evaluation of such risks is still lacking: existing benchmarks show coarse granularity, linguistic bias, and a neglect of multimodal privacy risks.To address these gaps, we introduce KoreaGEO, the first fine-grained, multimodal, and privacyaware benchmark for geolocation, built on Korean street views.The benchmark covers four socio-spatial clusters and nine place types with rich contextual annotations and two captioning styles that simulate real-world privacy exposure.To evaluate mainstream VLMs, we design a three-path protocol spanning imageonly, functional-caption, and high-risk-caption inputs, enabling systematic analysis of localization accuracy, spatial bias, and reasoning behavior.Results show that input modality exerts a stronger influence on localization precision and privacy exposure than model scale or architecture, with high-risk captions substantially boosting accuracy.Moreover, they highlight structural prediction biases toward core cities.

Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multilingual LLMs (MLLMs) to meet current demands, overlooking less-resourced languages (LRLs). This study proposed three strategies to enhance the performance of LRLs based on the publicly available MLLMs. First, the MLLM vocabularies of LRLs were expanded to enhance expressiveness. Second, bilingual data were used for pretraining to align the high- and less-resourced languages. Third, a high-quality small-scale instruction dataset was constructed and instruction-tuning was performed to augment the LRL. The experiments employed the Llama2 model and Korean was used as the LRL, which was quantitatively evaluated against other developed LLMs across eight tasks. Furthermore, a qualitative assessment was performed based on human evaluation and GPT4. Experimental results showed that our proposed Bllossom model exhibited superior performance in qualitative analyses compared to previously proposed Korean monolingual models.

Graph convolutional networks (GCNs) have brought remarkable progress in skeleton-based action recognition. However, high computational cost and large model size make models difficult to be applied in real-world embedded system. Specifically, GCN that is applied in automated surveillance system pre-require models such as pedestrian detection and human pose estimation. Therefore, each model should be computationally lightweight and whole process should be operated in real-time. In this paper, we propose two different joint-mapping modules to reduce the number of joint representations, alleviating a total computational cost and model size. Our models achieve better accuracy-latency trade-off compared to previous state-ofthe-arts on two datasets, namely NTU RGB+D and NTU RGB+D 120, demonstrating the suitability for practical applications. Furthermore, we measure the latency of the models by using TensorRT framework to compare the models from a practical perspective.

Human pose estimation is an interesting and underlying topic in various fields such as action recognition and human-computer interaction. Although many methods have been developed recently, they are still far from perfect in accuracy and speed at a time. In this paper, we propose a Classification-based Pose Estimation Network with Multi-task Learning (CPENML) based on the low-resolution feature map to improve accuracy and inference time simultaneously. The proposed CPENML consists of two ideas. Firstly, novel proposed keypoint and offset estimation tasks based on classification achieve better performance than regression. Secondly, the proposed Multi-Scale Network (MSN) makes robust feature maps and balances the keypoint and offset tasks to maximize performance. To prove the effectiveness of the proposed method, we conduct ablation studies on the COCO dataset for proposed ideas. Compared to benchmarks, we demonstrate the superiority of our proposed method on COCO dataset in terms of inference time and accuracy.

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.