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- 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.
- Bone Tissue Engineering in the Treatment of Bone Defects441
The 3D printing technology based on computer technology, aiming at designing personalized artificial scaffolds that can accurately fit bone defects, is summarized.
- Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation413
This paper proposes a novel Jointly Multiple Events Extraction framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information.
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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.
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- 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.
- 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.
- Social Influence Analysis: Models, Methods, and Evaluation71
This paper presents a survey of representative and state-of-the-art work in models, methods, and evaluation aspects related to SIA, and divides SIA models into microscopic and macroscopic models.
- Open Domain Event Extraction Using Neural Latent Variable Models67
This work considers open domain event extraction, the task of extracting unconstraint types of events from news clusters, and proposes a novel latent variable neural model, which is scalable to very large corpus.
- 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).
- Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey62
A reliable strategy for domains to choose more robust backbone LLMs for real-world applications is proposed, establishing connections between fundamental capabilities and specific domains, and evaluating the varying importance of different capabilities.
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- Cross-Lingual Abstractive Summarization with Limited Parallel Resources58
A novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting, Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes themonolingual summarization task a prerequisite of the CLS task.
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- Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis54
A novel variant of L STM is proposed, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTm cells in the context modeling stage before the attention mechanism, which can dynamically produce aspect- aware contextual representations.
- Task-oriented Word Embedding for Text Classification.54
A task-oriented word embedding method that regularizes the distribution of words to enable the embedding space to have a clear classification boundary and significantly outperforms the state-of-the-art methods.
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- Query Expansion Based on a Feedback Concept Model for Microblog Retrieval53
This work proposes a Feedback Concept Model for query expansion that expands the query using knowledge information derived from Probase so that the expanded one could better reflect users' search intent, which allows for microblog retrieval at a concept-level, rather than term-level.
- Fundamental Capabilities and Applications of Large Language Models: A Survey50
This survey provides a systematic introduction to LLM’ fundamental capabilities, encompassing their definitions, formation mechanisms, and practical applications, and proposes evaluation strategies tailored to the essential capabilities required in each domain, offering practical guidance for selecting suitable backbone LLMs in real-world applications.
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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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