1.6k citations · 6.6k across the 108 of their papers we have counts for
37 papers · 1 filter
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
Tianyi Tang, Junyi Li, Zhipeng Chen +9
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be co…
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Zhen Tian, Ting Bai, Zibin Zhang +4
With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely incr…
Visually-augmented pretrained language models for NLP tasks without images
Hangyu Guo, Kun Zhou, Wayne Xin Zhao +2
Although pre-trained language models~(PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense. Existin…
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
Kun Zhou, Xiao Liu, Yeyun Gong +4
Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-tr…
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +1
Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language questi…
Recent Advances in RecBole: Extensions with more Practical Considerations
Lanling Xu, Zhen Tian, Gaowei Zhang +10
RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update request…
Dense Text Retrieval based on Pretrained Language Models: A Survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren +1
Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language.…
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
Junyi Li, Tianyi Tang, Wayne Xin Zhao +2
We study the text generation task under the approach of pre-trained language models (PLMs). Typically, an auto-regressive (AR) method is adopted for generating texts in a token-by-…
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou, Yeyun Gong, Xiao Liu +8
Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninforma…
Privacy-Preserved Neural Graph Similarity Learning
Yupeng Hou, Wayne Xin Zhao, Yaliang Li +1
To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are fre…
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Yupeng Hou, Zhankui He, Julian McAuley +1
Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to e…
Modeling Two-Way Selection Preference for Person-Job Fit
Chen Yang, Yupeng Hou, Yang Song +3
Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers.…
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Zihan Lin, Xuanhua Yang, Xiaoyu Peng +4
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). Whil…
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Xiaolei Wang, Kun Zhou, Ji-Rong Wen +1
Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consist…
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan +16
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics a…
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Wayne Xin Zhao, Kun Zhou, Zheng Gong +7
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and…
Towards Universal Sequence Representation Learning for Recommender Systems
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao +3
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL…
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Zihan Lin, Hui Wang, Jingshu Mao +4
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides…
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Shanlei Mu, Yupeng Hou, Wayne Xin Zhao +2
Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based o…
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Tianyi Tang, Junyi Li, Wayne Xin Zhao +1
Pre-trained language models (PLMs) have achieved remarkable success in natural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs are pre-trained in an unsupervised…
Negative Sampling for Contrastive Representation Learning: A Review
Lanling Xu, Jianxun Lian, Wayne Xin Zhao +5
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great succe…
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
Xinyan Fan, Jianxun Lian, Wayne Xin Zhao +3
A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item…
Learning to Transfer Prompts for Text Generation
Junyi Li, Tianyi Tang, Jian-Yun Nie +2
Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in a data-scarce situation. The…
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +1
Commonsense reasoning in natural language is a desired ability of artificial intelligent systems. For solving complex commonsense reasoning tasks, a typical solution is to enhance…