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20172021
most citedSupervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic

43 citations · 69 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CL202143 cited

Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic

Wenjia Zhang, Lin Gui, Yulan He

As the digital news industry becomes the main channel of information dissemination, the adverse impact of fake news is explosively magnified. The credibility of a news report shoul…

cs.CL202111 cited

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

Lixing Zhu, Gabriele Pergola, Lin Gui +2

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intr…

cs.CL20211 cited

Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies

Gabriele Pergola, Elena Kochkina, Lin Gui +2

Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. Although an i…

cs.CL2020

CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering

Junru Lu, Gabriele Pergola, Lin Gui +2

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting…

cs.CL2019

TDAM: a Topic-Dependent Attention Model for Sentiment Analysis

Gabriele Pergola, Lin Gui, Yulan He

We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and emplo…

cs.CL201713 cited

A Question Answering Approach to Emotion Cause Extraction

Lin Gui, Jiannan Hu, Yulan He +3

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by r…