Papers by Zhengcong Fei
Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)
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| Challenge: | Existing methods to select appropriate stickers in open-domain dialogues have not been explored. |
| Approach: | They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. |
| Outcome: | The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines. |
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)
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| Challenge: | Existing image captioning models require large trainable parameters to bridge visual and textual representations. |
| Approach: | They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model. |
| Outcome: | The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models. |
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)
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| Challenge: | Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches. |
| Approach: | They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances. |
| Outcome: | The proposed model outperforms the DialoGPT on the dialogue generation task. |
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)
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| Challenge: | Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient. |
| Approach: | They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient . |
| Outcome: | The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation. |