Challenge: Existing word embeddings for Korean use the internal structure of words with subword information to improve the quality of word representations.
Approach: They introduce Basic, Tiniest Subword (BTS) units for Korean language that are inspired by Hangeul, the Korean writing system.
Outcome: The proposed framework outperforms the state-of-the-art Korean word embedding by 11.8% on all intrinsic and extrinsic tasks.

Similar Papers

KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters (2024.findings-acl)

Copied to clipboard

Challenge: Existing pre-trained language models for Korean have overlooked character representation principles . a new framework for Korean PLMs uses the invention principles of Hangeul to represent letters .
Approach: They propose a framework that uses Hangeul's invention principles to represent character in Korean . their method outperforms the state-of-the-art Korean PLM by 2.11% in five tasks .
Outcome: The proposed method outperforms the state-of-the-art Korean PLM by 2.11% in five Korean natural language understanding tasks.
Subword-level Word Vector Representations for Korean (P18-1)

Copied to clipboard

Challenge: Existing research on word vectors for English focuses on decomposing words into subword units and using subwords to improve performance.
Approach: They propose to decompose Korean words into the jamo-level, beyond the character-level . they develop Korean test sets for word similarity and analogy and make them publicly available .
Outcome: The proposed method outperforms word2vec and character-level skip-grams on similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)

Copied to clipboard

Challenge: Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models.
Approach: They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models .
Outcome: The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning.
SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models (2026.findings-acl)

Copied to clipboard

Challenge: Korean uses a featural writing system in which each character is composed of subcharacter units known as Jamo.
Approach: They propose a model-agnostic module that injects subcharacter compositional knowledge into Korean language models.
Outcome: a new module improves embeddings of Korean subwords with structural granularity . the module improve grammatical regularities and semantic cohesive variations .
Parameter-Efficient Korean Character-Level Language Modeling (2023.eacl-main)

Copied to clipboard

Challenge: Character-level language modeling has been shown to perform well on highly agglutinative or morphologically rich languages while using only a small fraction of the parameters required by (sub)word models.
Approach: They propose a “three-hot” embedding and decoding scheme that exploits the decomposability of Korean characters to model at the syllable level but using only jamo-level representations.
Outcome: The proposed model reduces the embedding parameters by 99.6% and does not lose translation quality compared to the baseline model.
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings (2021.eacl-main)

Copied to clipboard

Challenge: Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models.
Approach: They propose a Hangeul word embedding model that infers original word embeds from typos while maintaining high performance.
Outcome: The proposed model performs well against typos while maintaining high performance.
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization (2020.lrec-1)

Copied to clipboard

Challenge: a vocabulary size problem is unsolved in multilingual pre-training tasks.
Approach: They propose two algorithms that increase the elasticity of budget for building the vocabulary in Byte-Pair Encoding inspired tokenizers, and reduce the cost of supporting Korean in a multilingual model.
Outcome: The proposed method reduces the cost of supporting Korean in a multilingual model.
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities (N19-1)

Copied to clipboard

Challenge: Recent work has shown that contextualized word representations are a viable alternative to simple word prediction tasks.
Approach: They propose to use subword units and characters to model morphology, syntax, and semantics instead of word embeddings.
Outcome: The proposed representations are better for modeling syntax and more robust to noisy input.
Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja (D19-1)

Copied to clipboard

Challenge: Existing knowledge of Korean and Chinese is based on cultural and historical reasons.
Approach: They propose a method for improving Korean word representations using additional linguistic annotation by leveraging the fact that Hanja is closely related to Chinese.
Outcome: The proposed approach improves representations on a novel Korean news headline generation task.
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks (2020.aacl-main)

Copied to clipboard

Challenge: Traditionally, tokenization is the very first step in most text processing works.
Approach: They propose to use morphological segmentation followed by BPE for Korean NLP tasks . they empirically examine what is the best tokenization strategy for Korean to/from English .
Outcome: The proposed approach is best for Korean to/from English machine translation and natural language understanding tasks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations