  Slot labeling (SL) is a core component of task-oriented dialogue (ToD)
systems, where slots and corresponding values are usually language-, task- and
domain-specific. Therefore, extending the system to any new
language-domain-task configuration requires (re)running an expensive and
resource-intensive data annotation process. To mitigate the inherent data
scarcity issue, current research on multilingual ToD assumes that sufficient
English-language annotated data are always available for particular tasks and
domains, and thus operates in a standard cross-lingual transfer setup. In this
work, we depart from this often unrealistic assumption. We examine challenging
scenarios where such transfer-enabling English annotated data cannot be
guaranteed, and focus on bootstrapping multilingual data-efficient slot
labelers in transfer-free scenarios directly in the target languages without
any English-ready data. We propose a two-stage slot labeling approach (termed
TWOSL) which transforms standard multilingual sentence encoders into effective
slot labelers. In Stage 1, relying on SL-adapted contrastive learning with only
a handful of SL-annotated examples, we turn sentence encoders into
task-specific span encoders. In Stage 2, we recast SL from a token
classification into a simpler, less data-intensive span classification task.
Our results on two standard multilingual TOD datasets and across diverse
languages confirm the effectiveness and robustness of TWOSL. It is especially
effective for the most challenging transfer-free few-shot setups, paving the
way for quick and data-efficient bootstrapping of multilingual slot labelers
for ToD.
