论文标题
IITK在Semeval-2020任务10：用于重点选择的变压器
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
论文作者
论文摘要
本文介绍了为解决Semeval-2020任务10中提出的研究问题提出的系统：视觉媒体中的书面文本的重点选择。我们提出了一个端到端模型，该模型以输入文本为输入，并与每个单词相对应，给出了要强调的单词的概率。我们的结果表明，基于变压器的模型在此任务中特别有效。我们以0.810的成绩获得了最佳的匹配分数（在第2.2节中描述），并在排行榜上排名第三。
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. We propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized. Our results show that transformer-based models are particularly effective in this task. We achieved the best Matchm score (described in section 2.2) of 0.810 and were ranked third on the leaderboard.
