论文标题
CIRCE在Semeval-2020任务1：与上下文与上下文相关的单词表示
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
论文作者
论文摘要
本文介绍了对2020年Semeval-2020任务的获胜贡献1：无监督的词汇更改检测（子任务2）由UG Team Student Intern交付。我们提出了一个合奏模型，该模型基于无上下文和上下文依赖的单词表示。关键发现是（1）无上下文的单词表示是一个强大而稳健的基线，（2）句子分类目标可用于获得有用的上下文依赖上下文依赖的单词表示，并且（3）组合这些表示形式在某些数据集上提高了性能，同时降低了其他数据的性能。
This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others.
