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Tzu-Ray Su

dblp:205/2576 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-author

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
Information extraction and text analysis · 50% Deep learning architectures and training · 28% Representation and self-supervised learning · 22%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › multilingual NLP
cross-lingual word alignment		 0.412019
A Discriminative Neural Model for Cross-Lingual Word Alignment · EMNLP/IJCNLP (1) 2019
Machine learning › Representation and self-supervised learning › word representation › word embedding
chinese word embedding		 0.312017
Learning Chinese Word Representations From Glyphs Of Characters · EMNLP 2017

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

neural network · 0.4discriminative training · 0.4convolutional autoencoder · 0.3character embedding · 0.3
YearPublicationVenuePosition
2019 A Discriminative Neural Model for Cross-Lingual Word Alignment
abstract
Elias Stengel-Eskin, Tzu-ray Su, Matt Post, Benjamin Van Durme. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Elias Stengel-Eskin, Tzu-Ray Su, Matt Post, Benjamin Van Durme
 EMNLP/IJCNLP (1)2
2017 Learning Chinese Word Representations From Glyphs Of Characters
abstract
In this paper, we propose new methods to learn Chinese word representations.Chinese characters are composed of graphical components, which carry rich semantics.It is common for a Chinese learner to comprehend the meaning of a word from these graphical components.As a result, we propose models that enhance word representations by character glyphs.The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings.Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.
Tzu-Ray Su, Hung-yi Lee
 EMNLP1