24 citations · 88 across the 17 of their papers we have counts for
18 papers
Terminal-Bench-LILT: Multilingual Agentic Coding Benchmark Grounded in Language, Region, and Culture
Yunsu Kim, Kaden Uhlig, Ashwin Purohit +10
Most evaluations for coding agents are conducted exclusively in English, which does not reflect real-world multilingual deployment. We present Terminal-Bench-LILT, a suite of 300 a…
GAIA-v2-LILT: Multilingual Adaptation of Agent Benchmark beyond Translation
Yunsu Kim, Kaden Uhlig, Joern Wuebker
Agent benchmarks remain largely English-centric, while their multilingual versions are often built with machine translation (MT) and limited post-editing. We argue that, for agenti…
Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations
Baikjin Jung, Myungji Lee, Jong-Hyeok Lee +1
Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-qual…
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring
Heejin Do, Yunsu Kim, Gary Geunbae Lee
Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt…
Score-balanced Loss for Multi-aspect Pronunciation Assessment
Heejin Do, Yunsu Kim, Gary Geunbae Lee
With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress. Howe…
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing
Jihyun Lee, Seungyeon Seo, Yunsu Kim +1
We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11). DSTC11-Track2 aims to provide a benchmark for zero-shot, cross-domain, intent-set induction.…
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
Jihyun Lee, Chaebin Lee, Yunsu Kim +1
In dialogue state tracking (DST), labeling the dataset involves considerable human labor. We propose a new self-training framework for few-shot generative DST that utilize unlabele…
Hierarchical Pronunciation Assessment with Multi-Aspect Attention
Heejin Do, Yunsu Kim, Gary Geunbae Lee
Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels…
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions
Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to…
When and Why is Unsupervised Neural Machine Translation Useless?
Yunsu Kim, Miguel Graça, Hermann Ney
This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, w…
When and Why is Document-level Context Useful in Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus…
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
Yunsu Kim, Petre Petrov, Pavel Petrushkov +2
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to…
Generalizing Back-Translation in Neural Machine Translation
Miguel Graça, Yunsu Kim, Julian Schamper +2
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-t…
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
Yunsu Kim, Hendrik Rosendahl, Nick Rossenbach +3
We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoenc…
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Yunsu Kim, Yingbo Gao, Hermann Ney
Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocab…
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Yunsu Kim, Jiahui Geng, Hermann Ney
Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we…
Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes
Yunsu Kim, Julian Schamper, Hermann Ney
We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm t…
A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing
Yunsu Kim, Andreas Guta, Joern Wuebker +1
This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that th…