most citedA Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck

5 citations · 7 across the 2 of their papers we have counts for

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cs.CL20241 cited

CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models

Wentao Liu, Qianjun Pan, Yi Zhang +7

Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improvi…

cs.CL2024

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis

Zhenxiao Cheng, Jie Zhou, Wen Wu +2

Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-lev…

cs.CL20241 cited

Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models

Shunyu Liu, Jie Zhou, Qunxi Zhu +4

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in…

cs.CL2023

Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations

Zhenxiao Cheng, Jie Zhou, Wen Wu +2

Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models. However, the existing work has shown that the gr…

cs.CL20225 cited

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck

Jie Zhou, Qi Zhang, Qin Chen +2

Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achie…

cs.CL20212 cited

Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering

Jiayu Ding, Siyuan Wang, Qin Chen +1

Recent years have witnessed impressive advances in challenging multi-hop QA tasks. However, these QA models may fail when faced with some disturbance in the input text and their in…