LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Li, Irene; Feng, Aosong; Wu, Hao; Li, Tianxiao; Suzumura, Toyotaro; Dong, Ruihai

doi:10.48550/arxiv.2103.14620

Cited by 1 publication

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“…Another dataset from the domain of Hate-speech detection is HateXplain [17] which provide explanations for different tweets being hateful/offensive. Though there exist some methods that can generate explanations for multi-label classification [16,21], to the best of our knowledge, there exists no dataset that provides explanations in a multi-label setting, with separate explanations for different labels. The CAVES dataset developed in this paper is the first dataset containing such distinct explanations for each label assigned to a text (tweet).…”

Section: Multi-label Classification Explanation Generation and Summar...mentioning

confidence: 99%

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Poddar¹,

Samad²,

Mukherjee³

et al. 2022

Preprint

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Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. Our dataset and codes are available at: https://github.com/sohampoddar26/caves-data

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Section: Multi-label Classification Explanation Generation and Summar...mentioning

confidence: 99%

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Poddar¹,

Samad²,

Mukherjee³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Cited by 1 publication

References 0 publications

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

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