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Found 5 papers, 3 papers with code
Target-aware Abstractive Related Work Generation with Contrastive Learning
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST
From our recently released ASAD dataset, we provide the competitors with 55K tweets for training, and 20K tweets for validation, based on which the performance of participating teams are ranked on a leaderboard, https://www. kaggle. com/c/arabic-sentiment-analysis-2021-kaust.
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories.
