Papers by Zeyang Lei
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
Copied to clipboard
| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)
Copied to clipboard
Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li
| Challenge: | Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks. |
| Approach: | They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art on offline and online metrics. |
Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually. |
| Approach: | They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system. |
| Outcome: | The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
Copied to clipboard
| Challenge: | a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots. |
| Approach: | They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains . |
| Outcome: | The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
Copied to clipboard
| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
DT-QDC: A Dataset for Question Comprehension in Online Test (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to measure difficulty of questions are not accurate enough to guide learning. |
| Approach: | They propose to use a Chinese DT-QDC dataset to measure difficulty of questions and provide a new model that can improve the judgment of difficulty from different perspectives. |
| Outcome: | The proposed methods outperform baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE, and 28.2% on MSC on the new DT-QDC dataset. |
Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve persona consistency on high-quality human-labeled persona datasets face high cost and poor scalability. |
| Approach: | They propose a method to improve zero-shot persona consistency via in-context learning by pre-training a persona-augmented dialogue generation model and then using in-constant prompting mechanism to realize zero- shot persona customization. |
| Outcome: | The proposed method improves persona consistency without compromising coherence and informativeness in zero-shot settings. |