Papers by Chenlong Hu
One-class Text Classification with Multi-modal Deep Support Vector Data Description (2021.eacl-main)
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| Challenge: | Using multi-modal deep SVDD, we can build a much better description for target one-class data. |
| Approach: | They propose to extend uni-modal SVDD to multiple modal mSVDD and introduce a mechanism for incorporating negative supervision in the absence of real negative data. |
| Outcome: | The proposed model outperforms uni-modal SVDD and can get further improvements when negative supervision is incorporated. |
R2A-TLS: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Integration (2025.findings-emnlp)
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| Challenge: | Existing methods struggle to capture coherent event narratives due to fragmented descriptions . Existing approaches accumulate noise through iterative retrieval strategies that lack relevance evaluation. |
| Approach: | They propose a reflective retrieval-augmented timeline summarization with Causal-Semantic Intergration approach for open-domain timeline summarizing . |
| Outcome: | The proposed approach outperforms the best prior published approaches. |
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)
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| Challenge: | Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered . |
| Approach: | They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts. |
| Outcome: | The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script. |
A Simple and Effective Usage of Word Clusters for CBOW Model (2020.aacl-main)
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| Challenge: | Existing word clustering algorithms can be used to obtain word embeddings without additional language resources. |
| Approach: | They propose to replace infrequent input and output words with clusters to produce word embeddings. |
| Outcome: | The proposed method produces embeddings of frequent words and small amount of cluster embeddables, which can be fine-tuned on downstream tasks. |