HC$^2$L：用于跨语言口语理解的混合与协作对比学习
HC$^2$L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding
AI总结：
针对零样本跨语言口语理解中标签信息未被利用的问题，提出融合无监督与三类有监督对比学习的协作框架，在9种语言上取得新SOTA。
摘要：
用于零样本跨语言口语理解的最先进模型通过执行跨语言无监督对比学习，实现每条话语与其语码转换数据之间与标签无关的语义对齐。然而，该方法忽略了宝贵的意图/槽位标签，而这些标签信息有望帮助捕获标签感知的语义结构，进而利用有监督对比学习改进源语言和目标语言的语义。本文提出混合与协作对比学习来解决这一问题。除跨语言无监督对比学习外，我们设计了一种整体方法，利用源语言有监督对比学习、跨语言有监督对比学习和多语言有监督对比学习，以全面方式执行标签感知语义对齐。每一种有监督对比学习机制都包含单任务和联合任务场景。在我们的模型中，一种对比学习机制的输入会由其他机制增强。因此，总共四种对比学习机制相互协作，在训练过程的良性循环中学习更一致、更具判别性的表示。实验表明，我们的模型在9种语言上取得持续提升，达到新的最先进性能。
英文摘要：
State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic alignment between each utterance and its code-switched data. However, it ignores the precious intent/slot labels, whose label information is promising to help capture the label-aware semantics structure and then leverage supervised contrastive learning to improve both source and target languages' semantics. In this paper, we propose Hybrid and Cooperative Contrastive Learning to address this problem. Apart from cross-lingual unsupervised contrastive learning, we design a holistic approach that exploits source language supervised contrastive learning, cross-lingual supervised contrastive learning and multilingual supervised contrastive learning to perform label-aware semantics alignments in a comprehensive manner. Each kind of supervised contrastive learning mechanism includes both single-task and joint-task scenarios. In our model, one contrastive learning mechanism's input is enhanced by others. Thus the total four contrastive learning mechanisms are cooperative to learn more consistent and discriminative representations in the virtuous cycle during the training process. Experiments show that our model obtains consistent improvements over 9 languages, achieving new state-of-the-art performance.
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。
