40 citations · 91 across the 19 of their papers we have counts for
8 papers · 1 filter
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
Xuefei Jiang, Dairui Liu, Ruihai Dong
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the c…
Learning to Generalize for Cross-domain QA
Yingjie Niu, Linyi Yang, Ruihai Dong +1
There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Curr…
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Dairui Liu, Derek Greene, Ruihai Dong
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep le…
Pseudo-labelling Enhanced Media Bias Detection
Qin Ruan, Brian Mac Namee, Ruihai Dong
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but ef…
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Linyi Yang, Jiazheng Li, Pádraig Cunningham +3
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustnes…
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Irene Li, Aosong Feng, Hao Wu +3
Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider…
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Linyi Yang, Eoin M. Kenny, Tin Lok James Ng +3
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intellige…
Explainable Text-Driven Neural Network for Stock Prediction
Linyi Yang, Zheng Zhang, Su Xiong +4
It has been shown that financial news leads to the fluctuation of stock prices. However, previous work on news-driven financial market prediction focused only on predicting stock p…