Training code-switched language models is difficult due to lack of data and
complexity in the grammatical structure. Linguistic constraint theories have
been used for decades to generate artificial code-switching sentences to cope
with this issue. However, this require external word alignments or constituency
parsers that create erroneous results on distant languages. We propose a
sequence-to-sequence model using a copy mechanism to generate code-switching
data by leveraging parallel monolingual translations from a limited source of
code-switching data. The model learns how to combine words from parallel
sentences and identifies when to switch one language to the other. Moreover, it
captures code-switching constraints by attending and aligning the words in
inputs, without requiring any external knowledge. Based on experimental
results, the language model trained with the generated sentences achieves
state-of-the-art performance and improves end-to-end automatic speech
recognition.

为了解决训练混合语言模型的困难，本研究提出了一种基于序列 - 序列模型及 copy 机制的新型训练方法，通过有限的混合语言数据和单语数据的并行翻译生成需要的混合语言数据，且无需对齐或分析，实现了良好的表现，并显著提高了末端自动语音识别。