Transfer-Free Data-Efficient Multilingual Slot Labeling
Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen · 2023
Slot labeling (SL) is a core component of taskoriented dialogue (TOD) systems, where slots and corresponding values are usually language-, task-and domain-specific.Therefore, extending the system to any new language-domaintask 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 SLannotated 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.