  In this paper we propose a study of linguistic portability strategies of
large pre-trained language models (PLMs) used for open-domain dialogue systems
in a high-resource language for this task. In particular the target
low-resource language (L_T) will be simulated with French, as it lacks of
task-specific resources and allows our human evaluation, when the source
language (L_S) is English. For obvious reasons, recent works using such models
for open-domain dialogue are mostly developed in English. Yet building specific
PLMs for each possible target language supposes collecting new datasets and is
costly. For this reason, trying to leverage all existing resources (PLMs and
data) in both L_S and L_T , we wish to assess the performance achievable in L_T
with different approaches. The first two approaches evaluate the usage of
Neural Machine Translation (NMT) at different levels: TrainOnTarget where a L_S
dataset is translated before fine-tuning in L_T and TestOnSource where a L_S
model is coupled with NMT modules during inference. Then, the advent of BLOOM
[2], the world first open-access multilingual large PLM, allow researchers to
develop new approaches aiming to leverage not only the model's full
accessibility but also its multilingualism and translation abilities. In this
context the task is learned in L_S first and adapted to L_T using the MAD-X
Adapter architecture [16]. In the two sets of experiments models are evaluated
in spoken dialogue conditions with human and the strategies can be compared in
terms of perceived interaction quality.
