MIDAS在SemEval-2020任务10：使用标签分布学习和上下文嵌入进行强调选择
MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings
摘要：
本文介绍了我们提交给SemEval 2020 -任务10的书面文本中的重点选择。我们把这个重点选择问题看作是一个序列标签任务，我们用各种上下文嵌入模型来表示底层文本。我们还采用标签分布学习来解释注释者的分歧。我们对模型架构的选择、层的可训练性和不同的上下文嵌入进行了实验。我们表现最好的架构是不同模型的组合，整体匹配得分为0.783，在31个参赛团队中排名第15。最后，我们分析的结果，在词性标记，句子长度和单词排序。
英文摘要：
This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
