activity
20152022
most citedA Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings

5 citations · 17 across the 7 of their papers we have counts for

collaborators

16 papers

cs.CL2022

Zero-shot Cross-lingual Conversational Semantic Role Labeling

Han Wu, Haochen Tan, Kun Xu +3

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of…

cs.CL20225 cited

A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings

Haochen Tan, Wei Shao, Han Wu +2

Contrastive learning has shown great potential in unsupervised sentence embedding tasks, e.g., SimCSE. However, We find that these existing solutions are heavily affected by superf…

cs.CL20213 cited

EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information

Lei Huang, Jiecong Lin, Xiangtao Li +2

The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the…

cs.AI2020

Distributed Thompson Sampling

Jing Dong, Tan Li, Shaolei Ren +1

We study a cooperative multi-agent multi-armed bandits with M agents and K arms. The goal of the agents is to minimized the cumulative regret. We adapt a traditional Thompson Sampl…

cs.CL20201 cited

Semantic Role Labeling Guided Multi-turn Dialogue ReWriter

Kun Xu, Haochen Tan, Linfeng Song +4

For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its perform…

cs.LG2020

Federated Recommendation System via Differential Privacy

Tan Li, Linqi Song, Christina Fragouli

In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differ…