607 citations · 1.2k across the 25 of their papers we have counts for
25 papers
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…
CCGen: Explainable Complementary Concept Generation in E-Commerce
Jie Huang, Yifan Gao, Zheng Li +7
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang +5
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (N…
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Chen Liang, Haoming Jiang, Zheng Li +3
Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on tas…
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…
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…
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…
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…
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…
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…
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
Haoming Jiang, Bo Dai, Mengjiao Yang +2
Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turi…
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang +3
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate t…
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Yue Yu, Simiao Zuo, Haoming Jiang +3
Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fi…
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
Chen Liang, Yue Yu, Haoming Jiang +4
We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yie…
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
Jason Ge, Xingguo Li, Haoming Jiang +4
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear reg…
Deep Reinforcement Learning with Robust and Smooth Policy
Qianli Shen, Yan Li, Haoming Jiang +2
Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, whic…
Transformer Hawkes Process
Simiao Zuo, Haoming Jiang, Zichong Li +2
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets. These data often exhib…
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Haoming Jiang, Pengcheng He, Weizhu Chen +3
Transfer learning has fundamentally changed the landscape of natural language processing (NLP) research. Many existing state-of-the-art models are first pre-trained on a large text…
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
Haoming Jiang, Chen Liang, Chong Wang +1
Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains. However, this design lacks a…
Contextual Text Denoising with Masked Language Models
Yifu Sun, Haoming Jiang
Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models ar…
Meta Learning with Relational Information for Short Sequences
Yujia Xie, Haoming Jiang, Feng Liu +2
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from s…
On the Variance of the Adaptive Learning Rate and Beyond
Liyuan Liu, Haoming Jiang, Pengcheng He +4
The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization a…
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Minshuo Chen, Haoming Jiang, Wenjing Liao +1
Real world data often exhibit low-dimensional geometric structures, and can be viewed as samples near a low-dimensional manifold. This paper studies nonparametric regression of Höl…
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie, Minshuo Chen, Haoming Jiang +2
Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we…