论文标题
MIDAS在2020年Semeval-2020任务10：使用标签分布学习和上下文嵌入的重点选择
MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings
论文作者
论文摘要
本文介绍了我们提交给Semeval 2020的提交 - 关于书面文本中重点选择的任务10。我们将重点选择问题作为一个序列标记任务，在其中代表具有各种上下文嵌入模型的基础文本。我们还采用标签分发学习来说明注释分歧分析。我们尝试选择模型体系结构，层次训练性和不同上下文嵌入的选择。我们表现最好的架构是由不同型号组成的合奏，其总体匹配得分为0.783，使我们在31个参与的球队中排名第15。最后，我们根据语音标签，句子长度和单词顺序的部分分析结果。
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.
