Search Results for author:
Found 16 papers, 5 papers with code
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
In this multilingual approach, a typical setup is to use pairs of (image and English-text) and translation pairs.
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment
The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text.
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean
This study proposed three strategies to enhance the performance of LRLs based on the publicly available MLLMs.
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining
The current research direction in generative models, such as the recently developed GPT4, aims to find relevant knowledge information for multimodal and multilingual inputs to provide answers.
K-UniMorph: Korean Universal Morphology and its Feature Schema
This dataset adopts morphological feature schema from Sylak-Glassman et al. (2015) and Sylak-Glassman (2016) for the Korean language as we extract inflected verb forms from the Sejong morphologically analyzed corpus that is one of the largest annotated corpora for Korean.
Korean Named Entity Recognition Based on Language-Specific Features
In the paper, we propose a novel way of improving named entity recognition in the Korean language using its language-specific features.
Yet Another Format of Universal Dependencies for Korean
In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations
We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies).
Affordances in Grounded Language Learning
We present a novel methodology involving mappings between different modes of semantic representation.
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with {``}Multilingual Parsing from Raw Text to Universal Dependencies{''}.
Named Entity Corpus Construction using Wikipedia and DBpedia Ontology
In this paper, we propose a novel method to automatically build a named entity corpus based on the DBpedia ontology.
