ACL

Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing …
Exploring the Potential of Large Language Models in Computational Argumentation
Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. …
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend …
Towards the TopMost: A Topic Modeling System Toolkit
Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, …
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
In this paper, we introduce UniBridge (Cross-Lingual Transfer Learning with Optimized Embeddings and Vocabulary), a comprehensive …
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be …
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to …
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely …
Jointprop:Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation
Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. …
Zero-Shot Text Classification via Self-Supervised Tuning
Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to …