activity
20192023
most citedHarnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

151 citations · 240 across the 28 of their papers we have counts for

collaborators
Showing 2022Show all

12 papers · 1 filter

cs.CL2022★ 2 cited

FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery

Changlong Yu, Weiqi Wang, Xin Liu +6

Understanding users' intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework to…

cs.LG2022★ 22 cited

Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs

Ruijie Wang, Zheng Li, Dachun Sun +4

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entiti…

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.CV2022

Vision-Language Adaptive Mutual Decoder for OOV-STR

Jinshui Hu, Chenyu Liu, Qiandong Yan +4

Recent works have shown huge success of deep learning models for common in vocabulary (IV) scene text recognition. However, in real-world scenarios, out-of-vocabulary (OOV) words a…

cs.CV2022★ 1 cited

SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition

Dajian Zhong, Shujing Lyu, Palaiahnakote Shivakumara +4

Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced At…

cs.CL2022

Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

Yifan Gao, Qingyu Yin, Zheng Li +5

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages ha…

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.IR2022

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data

Rui Feng, Chen Luo, Qingyu Yin +3

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and product…

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…

cs.IR2022

RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

Ruijie Wang, Zheng Li, Danqing Zhang +4

With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. R…