Papers by Chunyuan Yuan
Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)
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| Challenge: | Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation . |
| Approach: | They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues . |
| Outcome: | The proposed method outperforms existing methods on a real-world dataset and brings economic benefits. |
A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)
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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law
| Challenge: | Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling. |
| Approach: | They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments. |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
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| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
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| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data. |
| Approach: | They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks. |
| Outcome: | The proposed model achieves new state-of-the-art on VAE language modeling benchmarks. |
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)
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| Challenge: | Existing studies for multi-label text classification do not explore label-specific semantic components from documents. |
| Approach: | They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels. |