The lack of publicly available evaluation data for low-resource languages
limits progress in Spoken Language Understanding (SLU). As key tasks like
intent classification and slot filling require abundant training data, it is
desirable to reuse existing data in high-resource languages to develop models
for low-resource scenarios. We introduce xSID, a new benchmark for
cross-lingual Slot and Intent Detection in 13 languages from 6 language
families, including a very low-resource dialect. To tackle the challenge, we
propose a joint learning approach, with English SLU training data and
non-English auxiliary tasks from raw text, syntax and translation for transfer.
We study two setups which differ by type and language coverage of the
pre-trained embeddings. Our results show that jointly learning the main tasks
with masked language modeling is effective for slots, while machine translation
transfer works best for intent classification.

本文提出了跨语言意图和槽位检测的新基准，称为 xSID，并探讨了使用联合学习方法和机器翻译转移学习来处理数据稀缺的情况。结果表明，对于槽位填充任务，联合学习任务加遮掩语言模型是有效的，而机器翻译转移学习对于意图分类任务效果最好。

从遮蔽语言建模到翻译：非英语辅助任务提高零样本口语理解

From Masked Language Modeling to Translation: Non-English Auxiliary  Tasks Improve Zero-shot Spoken Language Understanding

Convolutional neural networks (CNNs) based approaches for semantic alignment
and object landmark detection have improved their performance significantly.
Current efforts for the two tasks focus on addressing the lack of massive
training data through weakly- or unsupervised learning frameworks. In this
paper, we present a joint learning approach for obtaining dense correspondences
and discovering object landmarks from semantically similar images. Based on the
key insight that the two tasks can mutually provide supervisions to each other,
our networks accomplish this through a joint loss function that alternatively
imposes a consistency constraint between the two tasks, thereby boosting the
performance and addressing the lack of training data in a principled manner. To
the best of our knowledge, this is the first attempt to address the lack of
training data for the two tasks through the joint learning. To further improve
the robustness of our framework, we introduce a probabilistic learning
formulation that allows only reliable matches to be used in the joint learning
process. With the proposed method, state-of-the-art performance is attained on
several standard benchmarks for semantic matching and landmark detection,
including a newly introduced dataset, JLAD, which contains larger number of
challenging image pairs than existing datasets.

本文提出了一种基于卷积神经网络的联合学习方法，以在语义相似的图像中获得稠密对应和发现对象地标，在解决训练数据不足方面取得了显著成效，并引入了概率学习方法以提高框架的稳健性，该方法在语义匹配和地标检测的多个标准基准测试中取得了最新的最佳表现。