Code-Switching (CS) is referred to the phenomenon of alternately using words
and phrases from different languages. While today's neural end-to-end (E2E)
models deliver state-of-the-art performances on the task of automatic speech
recognition (ASR) it is commonly known that these systems are very
data-intensive. However, there is only a few transcribed and aligned CS speech
available. To overcome this problem and train multilingual systems which can
transcribe CS speech, we propose a simple yet effective data augmentation in
which audio and corresponding labels of different source languages are
concatenated. By using this training data, our E2E model improves on
transcribing CS speech and improves performance over the multilingual model, as
well. The results show that this augmentation technique can even improve the
model's performance on inter-sentential language switches not seen during
training by 5,03\% WER.

本文研究提出一种简单而有效的数据增强方法，以在缺乏大量语料库的情况下训练多语言系统，进而提高 Code-Switching 语音的自动识别水平。通过将不同源语言的音频和相应标签连结在一起训练神经网络端到端 (E2E) 模型，实现在跨句子语言转换方面的提高。

端到端语音识别中的语言无关代码切换

Language-agnostic Code-Switching in End-To-End Speech Recognition

Named entity recognition (NER) and relation extraction (RE) are two important
tasks in information extraction and retrieval (IE \& IR). Recent work has
demonstrated that it is beneficial to learn these tasks jointly, which avoids
the propagation of error inherent in pipeline-based systems and improves
performance. However, state-of-the-art joint models typically rely on external
natural language processing (NLP) tools, such as dependency parsers, limiting
their usefulness to domains (e.g. news) where those tools perform well. The few
neural, end-to-end models that have been proposed are trained almost completely
from scratch. In this paper, we propose a neural, end-to-end model for jointly
extracting entities and their relations which does not rely on external NLP
tools and which integrates a large, pre-trained language model. Because the
bulk of our model's parameters are pre-trained and we eschew recurrence for
self-attention, our model is fast to train. On 5 datasets across 3 domains, our
model matches or exceeds state-of-the-art performance, sometimes by a large
margin.

本文提出了一种基于神经元的端到端模型来联合提取实体和它们的关系，该模型不依赖于外部自然语言处理工具，而是集成了大量的预训练语言模型， 在三个领域的五个数据集上，我们的模型可以与最先进的性能相提并论，有时甚至具有更高的性能。