Question generation has recently shown impressive results in customizing
question answering (QA) systems to new domains. These approaches circumvent the
need for manually annotated training data from the new domain and, instead,
generate synthetic question-answer pairs that are used for training. However,
existing methods for question generation rely on large amounts of synthetically
generated datasets and costly computational resources, which render these
techniques widely inaccessible when the text corpora is of limited size. This
is problematic as many niche domains rely on small text corpora, which
naturally restricts the amount of synthetic data that can be generated. In this
paper, we propose a novel framework for domain adaptation called contrastive
domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from
question generation and domain-invariant learning to answer out-of-domain
questions in settings with limited text corpora. Here, we train a QA system on
both source data and generated data from the target domain with a contrastive
adaptation loss that is incorporated in the training objective. By combining
techniques from question generation and domain-invariant learning, our model
achieved considerable improvements compared to state-of-the-art baselines.

本研究提出了一种名为对比领域自适应问答（CAQA）的新型领域适应框架，该框架结合了问题生成和域不变学习技术，用于在文本语料库受限的情况下回答域外问题，并且相较于现有技术取得了显著的效果改善。

基于有限文本语料库的对比领域自适应对话回答

Contrastive Domain Adaptation for Question Answering using Limited Text  Corpora

Domain generalization is challenging due to the domain shift and the
uncertainty caused by the inaccessibility of target domain data. In this paper,
we address both challenges with a probabilistic framework based on variational
Bayesian inference, by incorporating uncertainty into neural network weights.
We couple domain invariance in a probabilistic formula with the variational
Bayesian inference. This enables us to explore domain-invariant learning in a
principled way. Specifically, we derive domain-invariant representations and
classifiers, which are jointly established in a two-layer Bayesian neural
network. We empirically demonstrate the effectiveness of our proposal on four
widely used cross-domain visual recognition benchmarks. Ablation studies
validate the synergistic benefits of our Bayesian treatment when jointly
learning domain-invariant representations and classifiers for domain
generalization. Further, our method consistently delivers state-of-the-art mean
accuracy on all benchmarks.

本文提出了一种基于概率框架和可分变贝叶斯推理的方法，将不确定性引入神经网络权重来处理由于目标域数据不可访问引起的域移位和不确定性挑战。实验结果表明，该方法在四个广泛使用的交叉域视觉识别基准测试中始终提供最先进的平均准确性。

更贝叶斯：具有不确定性的领域不变学习

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Learning models that gracefully handle distribution shifts is central to
research on domain generalization, robust optimization, and fairness. A
promising formulation is domain-invariant learning, which identifies the key
issue of learning which features are domain-specific versus domain-invariant.
An important assumption in this area is that the training examples are
partitioned into "domains" or "environments". Our focus is on the more common
setting where such partitions are not provided. We propose EIIL, a general
framework for domain-invariant learning that incorporates Environment Inference
to directly infer partitions that are maximally informative for downstream
Invariant Learning. We show that EIIL outperforms invariant learning methods on
the CMNIST benchmark without using environment labels, and significantly
outperforms ERM on worst-group performance in the Waterbirds and CivilComments
datasets. Finally, we establish connections between EIIL and algorithmic
fairness, which enables EIIL to improve accuracy and calibration in a fair
prediction problem.

EIIL 是一种用于域不变学习的通用框架，采用环境推断技术，可以推断出对下游不变学习最有帮助的分区，实现了较好的性能并且可以改进公平预测问题中的准确性和校准度。