Multilingual writers and speakers often alternate between two languages in a
single discourse, a practice called "code-switching". Existing sentiment
detection methods are usually trained on sentiment-labeled monolingual text.
Manually labeled code-switched text, especially involving minority languages,
is extremely rare. Consequently, the best monolingual methods perform
relatively poorly on code-switched text. We present an effective technique for
synthesizing labeled code-switched text from labeled monolingual text, which is
more readily available. The idea is to replace carefully selected subtrees of
constituency parses of sentences in the resource-rich language with suitable
token spans selected from automatic translations to the resource-poor language.
By augmenting scarce human-labeled code-switched text with plentiful synthetic
code-switched text, we achieve significant improvements in sentiment labeling
accuracy (1.5%, 5.11%, 7.20%) for three different language pairs
(English-Hindi, English-Spanish and English-Bengali). We also get significant
gains for hate speech detection: 4% improvement using only synthetic text and
6% if augmented with real text.

本篇论文研究了如何通过合成标记的混合文本来提高情感标签和仇恨言论检测的准确性，特别是对于那些涉及少数民族语言的语料库，该方法可以有效地通过从自动翻译的资源语言选择适当的标记跨度替换所选的子树来实现。