The popularity of transfer learning stems from the fact that it can borrow
information from useful auxiliary datasets. Existing statistical transfer
learning methods usually adopt a global similarity measure between the source
data and the target data, which may lead to inefficiency when only local
information is shared. In this paper, we propose a novel Bayesian transfer
learning method named "CONCERT" to allow robust local information transfer for
high-dimensional data analysis. A novel conditional spike-and-slab prior is
introduced in the joint distribution of target and source parameters for
information transfer. By incorporating covariate-specific priors, we can
characterize the local similarities and make the sources work collaboratively
to help improve the performance on the target. Distinguished from existing
work, CONCERT is a one-step procedure, which achieves variable selection and
information transfer simultaneously. Variable selection consistency is
established for our CONCERT. To make our algorithm scalable, we adopt the
variational Bayes framework to facilitate implementation. Extensive experiments
and a genetic data analysis demonstrate the validity and the advantage of
CONCERT over existing cutting-edge transfer learning methods. We also extend
our CONCERT to the logistical models with numerical studies showing its
superiority over other methods.

我们提出了一种新颖的贝叶斯迁移学习方法 CONCERT，可以用于高维数据分析，通过引入条件 spike-and-slab 先验来进行信息传输，而且具有变量选择一致性和传输效率的优势。

CONCERT: 带条件棱镜稀疏先验的协变量丰富的鲁棒局部信息传输

CONCERT: Covariate-Elaborated Robust Local Information Transfer with  Conditional Spike-and-Slab Prior

End-to-end spoken language understanding (SLU) remains elusive even with
current large pretrained language models on text and speech, especially in
multilingual cases. Machine translation has been established as a powerful
pretraining objective on text as it enables the model to capture high-level
semantics of the input utterance and associations between different languages,
which is desired for speech models that work on lower-level acoustic frames.
Motivated particularly by the task of cross-lingual SLU, we demonstrate that
the task of speech translation (ST) is a good means of pretraining speech
models for end-to-end SLU on both monolingual and cross-lingual scenarios.
By introducing ST, our models give higher performance over current baselines
on monolingual and multilingual intent classification as well as spoken
question answering using SLURP, MINDS-14, and NMSQA benchmarks. To verify the
effectiveness of our methods, we also release two new benchmark datasets from
both synthetic and real sources, for the tasks of abstractive summarization
from speech and low-resource or zero-shot transfer from English to French. We
further show the value of preserving knowledge from the pretraining task, and
explore Bayesian transfer learning on pretrained speech models based on
continual learning regularizers for that.

利用跨语言语音翻译 (ST) 作为预训练任务可提高单语和多语意图分类，口语问答的性能，并探索模型的贝叶斯迁移学习和持续学习惯性。