Papers by Changxi Zhu
Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy (2021.emnlp-main)
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| Challenge: | Existing methods for training dialogue policies rely on a single learning system, but it requires many rounds of interaction. |
| Approach: | They propose a complementary policy learning framework which exploits the complementary advantages of the episodic memory (EM) policy and the deep Q-network (DQN) policy. |
| Outcome: | The proposed framework outperforms existing methods relying on a single learning system on three dialogue datasets. |
A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning (2022.findings-naacl)
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| Challenge: | Existing training paradigms for dialogue policy learning with brute-force random sampling are expensive and lack reliable evaluation of difficulty scores. |
| Approach: | They propose a flexible adaptive curriculum learning framework that integrates curriculum learning with a generic global curriculum. |
| Outcome: | The proposed framework improves learning performance and efficiency on three public dialogue datasets. |
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks (D19-1)
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| Challenge: | Existing models ignore conflict opinions because they are sparse in the datasets. |
| Approach: | They propose a multi-label classification model with dual attention mechanism to address these problems by excluding conflict opinions from existing models. |
| Outcome: | The proposed model addresses the problem of exclusion of conflict opinions from the datasets. |