  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 \url{https://github.com/CrowdsourcingMining/TACMA}.
