activity
20182023
most citedOn the Variance of the Adaptive Learning Rate and Beyond

607 citations · 1.2k across the 25 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.CL2022★ 1 cited

Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding

Haoming Jiang, Tianyu Cao, Zheng Li +6

E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-tra…

cs.IR2022★ 1 cited

Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites

Simiao Zuo, Qingyu Yin, Haoming Jiang +4

E-commerce queries are often short and ambiguous. Consequently, query understanding often uses query rewriting to disambiguate user-input queries. While using e-commerce search too…

cs.LG2022

DiP-GNN: Discriminative Pre-Training of Graph Neural Networks

Simiao Zuo, Haoming Jiang, Qingyu Yin +3

Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then…

cs.LG2022★ 77 cited

Condensing Graphs via One-Step Gradient Matching

Wei Jin, Xianfeng Tang, Haoming Jiang +4

As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning mode…

cs.CL2022

SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models

Jingfeng Yang, Haoming Jiang, Qingyu Yin +3

Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often leng…

cs.AI2022

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Zijie Huang, Zheng Li, Haoming Jiang +6

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling…