EDBT 2026 Demo / reviewers in the wild / expert
Ka Ming Wong
dblp:212/2008
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
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
1 paper |
Machine translation · 50% Language models and text generation · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › multilingual language models
cross-lingual language models |
0.6 | 1 | 2022 | Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract) · AAAI 2022 |
Natural language and speech › Machine translation › unsupervised machine translation
unsupervised neural machine translation |
0.6 | 1 | 2022 | Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract) · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
layer-to-layer attention · 0.6cross-lingual language model · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract)abstractUnsupervised Neural Machines Translation is the most ideal method to apply to Cantonese and Chinese translation because parallel data is scarce in this language pair. In this paper, we proposed a method that combined a modified cross-lingual language model and performed layer to layer attention on unsupervised neural machine translation. In our experiments, we observed that our proposed method does improve the Cantonese to Chinese and Chinese to Cantonese translation by 1.088 and 0.394 BLEU scores. We finally developed a web service based on our ideal approach to provide Cantonese to Chinese Translation and vice versa. Ka Ming Wong, Richard Tzong-Han Tsai |
AAAI | 1 |