Code-mixing, the blending of linguistic elements from distinct languages to
form meaningful sentences, is common in multilingual settings, yielding hybrid
languages like Hinglish and Minglish. Marathi, India's third most spoken
language, often integrates English for precision and formality. Developing
code-mixed language systems, like Marathi-English (Minglish), faces resource
constraints. This research introduces a Marathi-English code-mixed text
generation algorithm, assessed with Code Mixing Index (CMI) and Degree of Code
Mixing (DCM) metrics. Across 2987 code-mixed questions, it achieved an average
CMI of 0.2 and an average DCM of 7.4, indicating effective and comprehensible
code-mixed sentences. These results offer potential for enhanced NLP tools,
bridging linguistic gaps in multilingual societies.

该研究提出了一种用于生成马拉地语 - 英语混合文本的算法，并通过 Code Mixing Index（CMI）和 Degree of Code Mixing（DCM）指标进行评估，结果表明该算法能够生成有效和可理解的混合语句子，为多语社会中的语言差距提供了潜在的增强 NLP 工具的可能性。

马拉地英语混合文本生成

Marathi-English Code-mixed Text Generation

Errors of machine learning models are costly, especially in safety-critical
domains such as healthcare, where such mistakes can prevent the deployment of
machine learning altogether. In these settings, conservative models -- models
which can defer to human judgment when they are likely to make an error -- may
offer a solution. However, detecting unusual or difficult examples is notably
challenging, as it is impossible to anticipate all potential inputs at test
time. To address this issue, prior work has proposed to minimize the model's
confidence on an auxiliary pseudo-OOD dataset. We theoretically analyze the
effect of confidence minimization and show that the choice of auxiliary dataset
is critical. Specifically, if the auxiliary dataset includes samples from the
OOD region of interest, confidence minimization provably separates ID and OOD
inputs by predictive confidence. Taking inspiration from this result, we
present data-driven confidence minimization (DCM), which minimizes confidence
on an uncertainty dataset containing examples that the model is likely to
misclassify at test time. Our experiments show that DCM consistently
outperforms state-of-the-art OOD detection methods on 8 ID-OOD dataset pairs,
reducing FPR (at TPR 95%) by 6.3% and 58.1% on CIFAR-10 and CIFAR-100, and
outperforms existing selective classification approaches on 4 datasets in
conditions of distribution shift.

机器学习的错误往往代价高昂，因此在卫生领域等关键领域，必须使用保守模型以确保安全；然而，检测到的异常或难以识别的例子通常具有挑战性，为此，本文提出了一种名为 DCN 的数据驱动置信度最小化方法，该方法在测试时最小化模型的置信度以增加模型的准确性。