  The time at which a message is communicated is a vital piece of metadata in
many real-world natural language processing tasks such as Topic Detection and
Tracking (TDT). TDT systems aim to cluster a corpus of news articles by event,
and in that context, stories that describe the same event are likely to have
been written at around the same time. Prior work on time modeling for TDT takes
this into account, but does not well capture how time interacts with the
semantic nature of the event. For example, stories about a tropical storm are
likely to be written within a short time interval, while stories about a movie
release may appear over weeks or months. In our work, we design a neural method
that fuses temporal and textual information into a single representation of
news documents for event detection. We fine-tune these time-aware document
embeddings with a triplet loss architecture, integrate the model into
downstream TDT systems, and evaluate the systems on two benchmark TDT data sets
in English. In the retrospective setting, we apply clustering algorithms to the
time-aware embeddings and show substantial improvements over baselines on the
News2013 data set. In the online streaming setting, we add our document encoder
to an existing state-of-the-art TDT pipeline and demonstrate that it can
benefit the overall performance. We conduct ablation studies on the time
representation and fusion algorithm strategies, showing that our proposed model
outperforms alternative strategies. Finally, we probe the model to examine how
it handles recurring events more effectively than previous TDT systems.
