  Recent advances in language modeling have enabled new conversational systems.
In particular, it is often desirable for people to make choices among specified
options when using such systems. We address this problem of reference
resolution, when people use natural expressions to choose between the entities.
For example, given the choice `Should we make a Simnel cake or a Pandan cake?'
a natural response from a dialog participant may be indirect: `let's make the
green one'. Such natural expressions have been little studied for reference
resolution. We argue that robustly understanding such language has large
potential for improving naturalness in dialog, recommendation, and search
systems. We create AltEntities (Alternative Entities), a new public dataset of
42K entity pairs and expressions (referring to one entity in the pair), and
develop models for the disambiguation problem. Consisting of indirect referring
expressions across three domains, our corpus enables for the first time the
study of how language models can be adapted to this task. We find they achieve
82%-87% accuracy in realistic settings, which while reasonable also invites
further advances.
