Code switching (CS) is a very common phenomenon in written and spoken
communication but one that is handled poorly by many natural language
processing applications. Looking to the application of building CS corpora, we
explore CS language identification (LID) for corpus building. We make the task
more realistic by scaling it to more languages and considering models with
simpler architectures for faster inference. We also reformulate the task as a
sentence-level multi-label tagging problem to make it more tractable. Having
defined the task, we investigate three reasonable models for this task and
define metrics which better reflect desired performance. We present empirical
evidence that no current approach is adequate and finally provide
recommendations for future work in this area.

通过建立代码切换语料库的应用，我们研究了代码切换语言识别，考虑到多种语言和较简单的模型架构以实现更快速推理。我们将任务重新定义为句子级多标签标注问题，以使其更易处理，并提出反映所需性能的度量指标。我们通过实证实现表明当前的方法都不足够，并在这一领域提供未来工作的建议。

代码切换语言识别更加困难

Code-Switched Language Identification is Harder Than You Think

In this study, we propose to evaluate the use of deep learning methods for
semantic classification at the sentence level to accelerate the process of
corpus building in the field of humanities and linguistics, a traditional and
time-consuming task. We introduce a novel corpus comprising around 2500
sentences spanning from 300 BCE to 900 CE including sexual semantics (medical,
erotica, etc.). We evaluate various sentence classification approaches and
different input embedding layers, and show that all consistently outperform
simple token-based searches. We explore the integration of idiolectal and
sociolectal metadata embeddings (centuries, author, type of writing), but find
that it leads to overfitting. Our results demonstrate the effectiveness of this
approach, achieving high precision and true positive rates (TPR) of
respectively 70.60% and 86.33% using HAN. We evaluate the impact of the dataset
size on the model performances (420 instead of 2013), and show that, while our
models perform worse, they still offer a high enough precision and TPR, even
without MLM, respectively 69% and 51%. Given the result, we provide an analysis
of the attention mechanism as a supporting added value for humanists in order
to produce more data.

通过对句子级别的语义分类使用深度学习方法进行评估，以加速人文学科和语言学领域的语料库构建过程，我们介绍了一个包含各种类型的句子（医学，情色等）的新型语料库，跨度从公元前 300 年到公元 900 年。我们评估了不同的句子分类方法和不同的输入嵌入层，并表明所有方法都一致优于简单的基于标记的搜索。我们探索了个体方言和社会方言的元数据嵌入（世纪，作者，文体）的整合，但发现这会导致过拟合。我们的结果表明了这种方法的有效性，使用 HAN 实现了高精度和真正阳性率（TPR）分别为 70.60％和 86.33％。我们评估了数据集大小对模型性能（2013 个句子降为 420 个句子）的影响，并证明了即使没有 MLM，我们的模型仍然具有足够高的精度和 TPR，分别为 69％和 51％。基于这个结果，我们提供了对注意力机制的分析作为对人文学者的支持附加价值，以产生更多的数据。