Learning effective language representations from crowdsourced labels is
crucial for many real-world machine learning tasks. A challenging aspect of
this problem is that the quality of crowdsourced labels suffer high intra- and
inter-observer variability. Since the high-capacity deep neural networks can
easily memorize all disagreements among crowdsourced labels, directly applying
existing supervised language representation learning algorithms may yield
suboptimal solutions. In this paper, we propose \emph{TACMA}, a
\underline{t}emporal-\underline{a}ware language representation learning
heuristic for \underline{c}rowdsourced labels with \underline{m}ultiple
\underline{a}nnotators. The proposed approach (1) explicitly models the
intra-observer variability with attention mechanism; (2) computes and
aggregates per-sample confidence scores from multiple workers to address the
inter-observer disagreements. The proposed heuristic is extremely easy to
implement in around 5 lines of code. The proposed heuristic is evaluated on
four synthetic and four real-world data sets. The results show that our
approach outperforms a wide range of state-of-the-art baselines in terms of
prediction accuracy and AUC. To encourage the reproducible results, we make our
code publicly available at https://github.com/CrowdsourcingMining/TACMA.

本文提出了 TACMA，一种用于多个注释者的众包标签的时态感知语言表示学习启发式方法，它利用注意机制明确地对内观察者的可变性进行建模，并从多个工作者那里计算并聚合每个样本的信心得分以解决工人之间的一致性问题。在四个合成数据集和四个真实世界数据集上进行评估，结果表明，该方法在预测准确性和 AUC 方面优于广泛的最先进基准。