  In human-human conversations, Context Tracking deals with identifying
important entities and keeping track of their properties and relationships.
This is a challenging problem that encompasses several subtasks such as slot
tagging, coreference resolution, resolving plural mentions and entity linking.
We approach this problem as an end-to-end modeling task where the
conversational context is represented by an entity repository containing the
entity references mentioned so far, their properties and the relationships
between them. The repository is updated turn-by-turn, thus making training and
inference computationally efficient even for long conversations. This paper
lays the groundwork for an investigation of this framework in two ways. First,
we release Contrack, a large scale human-human conversation corpus for context
tracking with people and location annotations. It contains over 7000
conversations with an average of 11.8 turns, 5.8 entities and 15.2 references
per conversation. Second, we open-source a neural network architecture for
context tracking. Finally we compare this network to state-of-the-art
approaches for the subtasks it subsumes and report results on the involved
tradeoffs.
