Papers by Hong-You Chen
Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs (2020.acl-main)
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| Challenge: | Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus. |
| Approach: | They propose a multi-modal model that extracts visual features from Chinese word glyphs to expand current word embedding space without accessing any corpus. |
| Outcome: | The proposed model can embed words in Chinese without accessing corpus without a corpus. |
Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
| Outcome: | The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2025.findings-emnlp)
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Xinze Wang, Chen Chen, Yinfei Yang, Hong-You Chen, Bowen Zhang, Aditya Pal, Xiangxin Zhu, Xianzhi Du
| Challenge: | Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. |
| Approach: | They propose an alternative training strategy that converts a dense CLIP model into a sparse MoE architecture. |
| Outcome: | The proposed training strategy outperforms dense models on COCO and Flickr30k benchmarks. |
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (D18-1)
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| Challenge: | Lexicon relation extraction given distributional representation of words is an important topic in NLP. |
| Approach: | They propose to use a word relation autoencoder to extract hypernyms from vocabularies . they propose to analyze the pollution and construct an indicator to measure it . |
| Outcome: | The proposed model outperforms the competitors on several hypernym-like lexicon datasets. |