  In the slot-filling paradigm, where a user can refer back to slots in the
context during the conversation, the goal of the contextual understanding
system is to resolve the referring expressions to the appropriate slots in the
context. In this paper, we build on the context carryover
system~\citep{Naik2018ContextualSC}, which provides a scalable multi-domain
framework for resolving references. However, scaling this approach across
languages is not a trivial task, due to the large demand on acquisition of
annotated data in the target language. Our main focus is on cross-lingual
methods for reference resolution as a way to alleviate the need for annotated
data in the target language. In the cross-lingual setup, we assume there is
access to annotated resources as well as a well trained model in the source
language and little to no annotated data in the target language. In this paper,
we explore three different approaches for cross-lingual transfer \textemdash~\
delexicalization as data augmentation, multilingual embeddings and machine
translation. We compare these approaches both on a low resource setting as well
as a large resource setting. Our experiments show that multilingual embeddings
and delexicalization via data augmentation have a significant impact in the low
resource setting, but the gains diminish as the amount of available data in the
target language increases. Furthermore, when combined with machine translation
we can get performance very close to actual live data in the target language,
with only 25\% of the data projected into the target language.
