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Emily Chang

dblp:167/3301 · DBLP profile ↗
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4ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Transfer learning and domain adaptation · 36% Language models and text generation · 32% Information extraction and text analysis · 32%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 50% Interaction techniques and input · 50%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
large language model evaluation
1.012026
ChiKhaPo: A Large-Scale Multilingual Benchmark for Evaluating Lexical Comprehension and Generation in Large Language Models · ACL (1) 2026
Machine learning › Transfer learning and domain adaptation › language adaptation
dialect adaptation
0.912025
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models · ACL (1) 2025
Virtual and augmented reality
immersive interaction
0.212015
Marioneta: Virtual puppeteer experience · VR 2015
Interaction techniques and input › post-WIMP interaction › embodied interaction
full-body interaction
0.112015
Marioneta: Virtual puppeteer experience · VR 2015
Ubiquitous computing and smart environments › public displays
museum installations
0.112015
Marioneta: Virtual puppeteer experience · VR 2015

Methods — techniques the papers use, named apart from their topics

multilingual benchmarking · 1.0skeletal tracking · 0.4
YearPublicationVenuePosition
2026 ChiKhaPo: A Large-Scale Multilingual Benchmark for Evaluating Lexical Comprehension and Generation in Large Language Models
abstract
Existing benchmarks for large language models (LLMs) are largely restricted to high-or mid-resource languages, and often evaluate performance on higher-order tasks in reasoning and generation.However, plenty of evidence points to the fact that LLMs lack basic linguistic competence in the vast majority of the world's 3800+ written languages.We introduce ChiKhaPo, consisting of eight subtasks of varying difficulty designed to evaluate the lexical comprehension and generation abilities of generative models.ChiKhaPo draws on existing lexicons, monolingual data, and bitext, and provides coverage for 2700+ languages for two word-translation-based subtasks, surpassing any existing benchmark in terms of language coverage.We further show that six SOTA models struggle on our benchmark, and discuss the factors contributing to performance scores, including language family, language resourcedness, task, and comprehension versus generation directions.With ChiKhaPo, we hope to enable and encourage the massively multilingual benchmarking of LLMs.
Emily Chang, Niyati Bafna
ACL (1)1
2025 DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models
abstract
Niyati Bafna, Emily Chang, Nathaniel Romney Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, Hale Sirin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Niyati Bafna, Emily Chang, Nathaniel R. Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, Hale Sirin
ACL (1)2
2015 Marioneta: Virtual puppeteer experience
abstract
Marioneta is an installation for the Children's Museum of Pittsburgh which uses the Microsoft Kinect v2 to allow guests to embody a collection of antique puppets in a virtual environment. Final installation in the museum is shown in Fig 1. The focus is on creating an experience wherein elements in the world react to the users' actions through these puppets[1]. Puppet models in the experience are based on a collection of puppets donated to the museum by Margo Lovelace. The original puppets are carefully exhibited in a large display case on the wall in the museum. Many of the puppets in the museum's collection are antiques, fragile or valuable and not suited to hands-on play by the museum's young visitors. Marioneta uses technology to make museum puppets available for imaginative and interesting play[2]. The experience is composed of auto-rotating seasonal stages and season related interactive objects that have visual and audial feedback. Users can throw a pumpkin in fall, pick up an ice ball in winter, play with cowbells in spring, and break a lantern filled with fireflies in summer. One of the stage scenes is shown in Fig 2. Marioneta is an updated version of Virpets, which began in 2001 and remained over 10 years in the museum[3].
Hyunghwan Byun, Emily Chang, Maria Alejandra Montenegro, Alexander Moser, Christina Tarn, Shirley J. Saldamarco, Ruth Comley
VR2
2014 Transition-based Knowledge Graph Embedding with Relational Mapping Properties
Emily Chang, Thomas Fang Zheng
PACLIC3