Papers by Weichao Zhang
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)
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| Challenge: | Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. |
| Approach: | They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation. |
| Outcome: | The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets. |
HyQE: Ranking Contexts with Hypothetical Query Embeddings (2024.findings-emnlp)
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| Challenge: | Existing approaches to rank contexts rely on similarity between contexts and queries, but these methods are limited by the number of candidate contexts. |
| Approach: | They propose a scalable ranking framework that combines embedding similarity and large language models without fine-tuning. |
| Outcome: | The proposed framework improves the performance across multiple benchmarks. |
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)
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| Challenge: | Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions. |
| Approach: | They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation. |
| Outcome: | The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions. |
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method (2024.emnlp-main)
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| Challenge: | Existing methods to detect text in training corpus are limited due to their low token probabilities. |
| Approach: | They propose a method to calibrate token probabilities for pretraining data detection by using a divergence-based calibration method. |
| Outcome: | The proposed method significantly outperforms existing methods on Chinese text on English-language benchmarks and patents. |