Towards human-level visual understanding, visual commonsense generation has been introduced to generate commonsense inferences beyond images.However, current research on visual commonsense generation has overlooked an important human cognitive ability: generating descriptive and diverse inferences.In this work, we propose a novel visual commonsense generation framework, called DIVE, which aims to improve the descriptiveness and diversity of generated inferences.DIVE involves two methods, generic inference filtering and contrastive retrieval learning, which address the limitations of existing visual commonsense resources and training objectives.Experimental results verify that DIVE outperforms state-ofthe-art models for visual commonsense generation in terms of both descriptiveness and diversity, while showing a superior quality in generating unique and novel inferences.Notably, DIVE achieves human-level descriptiveness and diversity on Visual Commonsense Graphs.Furthermore, human evaluations confirm that DIVE aligns closely with human judgments on descriptiveness and diversity 1 .

Huiju Kim, Youjin Kang, SangKeun Lee. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

We introduce Basic, Tiniest Subword (BTS) units for the Korean language, which are inspired by the invention principle of Hangeul, the Korean writing system.Instead of relying on 51 Korean consonant and vowel letters, we form the letters from BTS units by adding strokes or combining them.To examine the impact of BTS units on Korean language processing, we develop a novel BTSbased word embedding framework that is readily applicable to various models.Our experiments reveal that BTS units significantly improve the performance of Korean word embedding on all intrinsic and extrinsic tasks in our evaluation.In particular, BTS-based word embedding outperforms the state-of-theart Korean word embedding by 11.8% in word analogy.We further investigate the unique advantages provided by BTS units through indepth analysis.Our code is available at https: //github.com/irishev/BTS.