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- Xiaoming LiSuggested from co-authorship
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Works205 from public data
- InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training454
An information-theoretic framework that formulates cross-lingual language model pre- training as maximizing mutual information between multilingual-multi-granularity texts is presented and a new pre-training task based on contrastive learning is proposed.
- Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System259
This paper proposes a novel multi-level cross-view contrastive learning mechanism, named MCCLK, which comprehensively considers three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views, and a k-Nearest-Neighbor item-item semantic graph construction module is proposed.
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- Cross-Lingual Natural Language Generation via Pre-Training142
Experimental results on question generation and abstractive summarization show that the model outperforms the machine-translation-based pipeline methods for zero-shot cross-lingual generation and improves NLG performance of low-resource languages by leveraging rich-resource language data.
- XLM-E: Cross-lingual Language Model Pre-training via ELECTRA141
This paper introduces ELECTRA-style tasks to cross-lingual language model pre-training, and pretrain the model, named as XLM-E, on both multilingual and parallel corpora, which outperforms the baseline models on various cross-lingsual understanding tasks with much less computation cost.
- Multi-View Intent Disentangle Graph Networks for Bundle Recommendation130
A novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of user intent and items’ associations at the finer granularity is proposed.
- Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning120
This paper focuses on exploring contrastive learning in KGR and proposes a novel multi-level interactive contrastiveLearning mechanism, which conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action.
- Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking118
The ReaLiSe model tackles the CSC task by capturing the semantic, phonetic and graphic information of the input characters, and selectively mixing the information in these modalities to predict the correct output.
- BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis106
A Bi-Syntax aware Graph Attention Network that fully exploits the syntax information of the constituent tree of a sentence to model the sentiment-aware context of every single aspect and the sentiment relations across aspects (called inter-context) for learning.
- MLS3RDUH: Deep Unsupervised Hashing via Manifold based Local Semantic Similarity Structure Reconstructing103
This paper proposes a novel deep unsupervised hashing method, called MLS3RDUH, which can reduce the noisy datapoints to further enhance retrieval performance and outperforms the state-of-the-art baselines.
- Deep Cross-Modal Hashing With Hashing Functions and Unified Hash Codes Jointly Learning99
A novel end-to-end Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning (DCHUC) is proposed, which outperforms the state-of-the-art cross-modal hashing methods.
- Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment87
This paper introduces denoising word alignment as a new cross-lingual pre-training task that can serve as a pretrained word aligner, which achieves reasonably low error rate on the alignment benchmarks.
- Food recommendation with graph convolutional network83
This work proposes a novel model Food recommendation with Graph Convolutional Network (FGCN), which exploits ingredient-ingredient, ingredient-recipe, and recipe-user relations deeply and could alleviate the sparsity issue in food recommendation.
- mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs82
Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5, and proposes a partially non-autoregressive objective for text-to-text pre-training.
- SSHLDA: A Semi-Supervised Hierarchical Topic Model65
A semi-supervised hierarchical topic model which aims to explore new topics automatically in the data space while incorporating the information from observed hierarchical labels into the modeling process, called Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA).
- Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent62
This paper proposes a novel fully zero-013 shot autonomous TOD agent, named AutoTOD, where all the delicate modules in traditional TOD systems are deprecated and all it needs is a general-purpose instruction-following lan-017 guage model (e.g. GPT-4).
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- Multi-level Contrastive Learning Framework for Sequential Recommendation57
The proposed MCLSR outperforms the state-of-the-art methods consistently and learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level).
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- Automatic labeling hierarchical topics51
This paper proposes two effective algorithms that automatically assign concise labels to each topic in a hierarchy by exploiting sibling and parent-child relations among topics and shows that the inter-topic relation is effective in boosting topic labeling accuracy.
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- Partial-Softmax Loss based Deep Hashing45
A novel Partial-Softmax Loss based Deep Hashing, called PSLDH, is proposed to generate high-quality hash codes and outperforms the state-of-the-art baselines in image retrieval task.
- Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks41
A novel neural network approach for person- Job fit is proposed, which estimates person-job fit from candidate profile and related recruitment history with co-attention neural networks (named PJFCANN), and the historical successful recruitment records are introduced to enrich the features of resumes and job postings and strengthen the current matching process.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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