Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

Autor: Choi, ChangSu, Jeong, Yongbin, Park, Seoyoon, Won, InHo, Lim, HyeonSeok, Kim, SangMin, Kang, Yejee, Yoon, Chanhyuk, Park, Jaewan, Lee, Yiseul, Lee, HyeJin, Hahm, Younggyun, Kim, Hansaem, Lim, KyungTae

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 curre

Externí odkaz: http://arxiv.org/abs/2403.10882

Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean

Autor: Oh, Tae Hwan, Han, Ji Yoon, Choe, Hyonsu, Park, Seokwon, He, Han, Choi, Jinho D., Han, Na-Rae, Hwang, Jena D., Kim, Hansaem

In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean g

Externí odkaz: http://arxiv.org/abs/2005.12898

Open BIM-based quantity take-off system for schematic estimation of building frame in early design stage

Autor: Choi, Jungsik, Kim, Hansaem, Kim, Inhan

Publikováno v: In Journal of Computational Design and Engineering January 2015 2(1):16-25

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Improving code-switched ASR with linguistic information

Autor: Chi, Jie, Bell, Peter

Publikováno v: Chi, J & Bell, P 2022, Improving code-switched ASR with linguistic information . in N Calzolari, C-R Huang, H Kim, J Pustejovsky, L Wanner, K-S Choi, P-M Ryu, H-H Chen, L Donatelli, H Ji, S Kurohashi, P Paggio, N Xue, S Kim, Y Hahm, Z He, T K Lee, E Santus, F Bond & S-H Na (eds), Proceedings of the 29th International Conference on Computational Linguistics . vol. 29, COLING, no. 1, vol. 29, pp. 7171-7176, The 29th International Conference on Computational Linguistics, 2022, Gyeongju, Korea, Democratic People's Republic of, 12/10/22 . < https://aclanthology.org/2022.coling-1.627/ >

This paper seeks to improve the performance of automatic speech recognition (ASR) systems operating on code-switched speech. Code-switching refers to the alternation of languages within a conversation, a phenomenon that is of increasing importance co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3094::d9157ccbc0d252413944a59e484793e5
https://hdl.handle.net/20.500.11820/347e6467-61df-4b79-82a8-18bf19b17a12