Papers by Wenquan Wu
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)
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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. |
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)
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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu
| Challenge: | Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks. |
| Approach: | They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations. |
| Outcome: | The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI. |
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations (2022.emnlp-main)
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Chujie Zheng, Jinfeng Zhou, Yinhe Zheng, Libiao Peng, Zhen Guo, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Minlie Huang
| Challenge: | Existing methods for detecting dialogue contradictions are difficult due to contextualization nature of conversations. |
| Approach: | They propose a benchmark for Contradiction Detection in Chinese Conversations . they use automatic conversation generation to simulate common user behaviors . |
| Outcome: | The proposed benchmark simulated the user behaviors that trigger chatbots to make contradictions . the results show that the current state-of-the-art chatbot can be easily goaded into making contradictions. |
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022.findings-acl)
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| Challenge: | Existing persona dialogue datasets and models can build long-term relationships with humans . however, current open-domain dialogue systems cannot build long relationships with users . |
| Approach: | They propose a long-term memory conversation dataset and a dialogue generation framework with long-Term memory mechanism to extract and continuously update long-time persona memory. |
| Outcome: | The proposed system outperforms baselines in terms of long-term dialogue consistency . the proposed system can build long-lasting relationships between humans and bots . |
What Deserves Memory: Adaptive Memory Distillation for LLM Agents (2026.acl-long)
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| Challenge: | Existing memory systems rely on predefined heuristics such as importance scores, emotional tags, or factual templates, encoding designer intuition rather than learning from the data itself. |
| Approach: | They propose an adaptive memory distillation framework that casts the assessment of the experience’s future utility as a matter of predictability. |
| Outcome: | The proposed framework achieves strong performance, efficiency, and storage reduction. |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2021.findings-acl)
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| Challenge: | PLATO-2 is a high-quality open-domain chatbot that can generate one-to-many mappings and improve response quality. |
| Approach: | They propose a curriculum learning process to build a high-quality open-domain chatbot . they use a coarse-grained generation model and latent variables to train a generative model . |
| Outcome: | The proposed model improves on Chinese and English data and can generate diverse responses and select the best response. |
Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)
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| 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. |