activity
20192023
most citedDialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning

2 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing

Ziming Huang, Zhuoxuan Jiang, Ke Wang +3

Currently, human-bot symbiosis dialog systems, e.g., pre- and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall effici…

cs.CL2022★ 1 cited

Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning

Zhuoxuan Jiang, Lingfeng Qiao, Di Yin +2

Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-s…

cs.CV2022

OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification

Ye Liu, Lingfeng Qiao, Di Yin +4

Scene segmentation and classification (SSC) serve as a critical step towards the field of video structuring analysis. Intuitively, jointly learning of these two tasks can promote e…

cs.CL2022★ 1 cited

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

Yuan Liang, Zhuoxuan Jiang, Di Yin +1

In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issu…

cs.IR2021

Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

Sanshi Yu, Zhuoxuan Jiang, Dong-Dong Chen +4

Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with th…

cs.CL2019★ 2 cited

DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning

Zhuoxuan Jiang, Ziming Huang, Dong Sheng Li +1

In end-to-end dialogue modeling and agent learning, it is important to (1) effectively learn knowledge from data, and (2) fully utilize heterogeneous information, e.g., dialogue ac…