无迁移的高数据效率多语言槽位标注
Transfer-Free Data-Efficient Multilingual Slot Labeling
AI总结：
针对多语言任务导向对话中无英文数据时的槽位标注难题，提出两阶段方法TWOSL，将句子编码器转为跨度编码器并重构为跨度分类，在少样本无迁移场景下高效且鲁棒。
摘要：
槽位标注（SL）是任务导向对话（ToD）系统的核心组件，其中槽位及其对应值通常是语言、任务和领域特定的。因此，将系统扩展到任何新的语言-领域-任务配置都需要（重新）运行昂贵且资源密集的数据标注过程。为缓解固有的数据稀缺问题，当前多语言ToD研究假设特定任务和领域始终有充足的英文标注数据可用，从而在标准的跨语言迁移设置下运行。在本工作中，我们摒弃了这一往往不切实际的假设。我们考察了无法保证此类支持迁移的英文标注数据的挑战性场景，并专注于在无迁移场景下直接在目标语言中引导数据高效的多语言槽位标注器，无需任何英文现成数据。我们提出了一种两阶段槽位标注方法（称为TWOSL），将标准的多语言句子编码器转化为有效的槽位标注器。在第一阶段，依靠仅含少量SL标注示例的SL自适应对比学习，我们将句子编码器转化为任务特定的跨度编码器。在第二阶段，我们将SL从词元分类重构为更简单、数据需求更少的跨度分类任务。我们在两个标准多语言TOD数据集及多种语言上的结果证实了TWOSL的有效性和鲁棒性。它对于最具挑战性的无迁移少样本设置尤为有效，为快速且数据高效地引导用于ToD的多语言槽位标注器铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
