  Anaphoric expressions, such as pronouns and referential descriptions, are
situated with respect to the linguistic context of prior turns, as well as, the
immediate visual environment. However, a speaker's referential descriptions do
not always uniquely identify the referent, leading to ambiguities in need of
resolution through subsequent clarificational exchanges. Thus, effective
Ambiguity Detection and Coreference Resolution are key to task success in
Conversational AI. In this paper, we present models for these two tasks as part
of the SIMMC 2.0 Challenge (Kottur et al. 2021). Specifically, we use TOD-BERT
and LXMERT based models, compare them to a number of baselines and provide
ablation experiments. Our results show that (1) language models are able to
exploit correlations in the data to detect ambiguity; and (2) unimodal
coreference resolution models can avoid the need for a vision component,
through the use of smart object representations.
