Author name cluster

Zefeng Cai

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2023 Conference Paper

Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

Linlin Wang
Zefeng Cai
Gerard de Melo
Zhu Cao
Liang He

Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. Extensive experiments demonstrate that our method generates high-quality explanations and achieves new state-of-the-art results in diverse domains.

PDF Details DOI

ICLR Conference 2023 Conference Paper

HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization

Zefeng Cai
Chongyang Tao
Tao Shen 0001
Can Xu
Xiubo Geng
Xin Alex Lin
Liang He 0001
Daxin Jiang

Recently, large-scale text retrieval has made impressive progress, facilitating both information retrieval and downstream knowledge-intensive tasks (e.g., open-domain QA and dialogue). With a moderate amount of data, a neural text retriever can outperform traditional methods such as BM25 by a large step. However, while being applied to out-of-domain data, the performance of a neural retriever degrades considerably. Therefore, how to enable a retriever to perform more robustly across different domains or tasks and even show strong zero-shot transfer ability is critical for building scalable IR systems. To this end, we propose HypeR, a hyper-prompted training mechanism to enable uniform retrieval across tasks of different domains. Specifically, our approach jointly trains the query encoder with a shared prompt-based parameter pool and a prompt synthesizer that dynamically composes hyper-prompt for encoding each query from different tasks or domains. Besides, to avoid the mode collapse of prompt attention distribution for different queries, we design a contrastive prompt regularization that promotes the mode of prompt attention to be aligned and uniform. Through multi-task hyper-prompted training, our retriever can master the ability to dynamically represent different types of queries and transfer knowledge across different domains and tasks. Extensive experiments show our model attains better retrieval performance across different tasks and better zero-shot transfer ability compared with various previous methods.

Details

IJCAI Conference 2022 Conference Paper

PCVAE: Generating Prior Context for Dialogue Response Generation

Zefeng Cai
Zerui Cai

Conditional Variational AutoEncoder (CVAE) is promising for modeling one-to-many relationships in dialogue generation, as it can naturally generate many responses from a given context. However, the conventional used continual latent variables in CVAE are more likely to generate generic rather than distinct and specific responses. To resolve this problem, we introduce a novel discrete variable called prior context which enables the generation of favorable responses. Specifically, we present Prior Context VAE (PCVAE), a hierarchical VAE that learns prior context from data automatically for dialogue generation. Meanwhile, we design Active Codeword Transport (ACT) to help the model actively discover potential prior context. Moreover, we propose Autoregressive Compatible Arrangement (ACA) that enables modeling prior context in autoregressive style, which is crucial for selecting appropriate prior context according to a given context. Extensive experiments demonstrate that PCVAE can generate distinct responses and significantly outperforms strong baselines.

PDF Details DOI