35 citations · 43 across the 4 of their papers we have counts for
6 papers
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?
Jaeyoul Shin, Taeuk Kim, Sang-goo Lee
We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attentio…
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
Taeuk Kim, Jihun Choi, Daniel Edmiston +1
With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings. In…
Summary Level Training of Sentence Rewriting for Abstractive Summarization
Sanghwan Bae, Taeuk Kim, Jihoon Kim +1
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then parap…
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
Kang Min Yoo, Taeuk Kim, Sang-goo Lee
We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning…
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
Jihun Choi, Taeuk Kim, Sang-goo Lee
We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding--based approaches that consider each sequence sep…
A Syllable-based Technique for Word Embeddings of Korean Words
Sanghyuk Choi, Taeuk Kim, Jinseok Seol +1
Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are…