Code-switched emotion detection, a task analyzing the emotion in code-switched texts, has gain increasing research attention within recent years. Prior works utilize various neural models with sophisticated features to pursuit high performance of the task, while they still overlook some crucial characteristics of the code-switched texts. In this work, we present an innovative approach for improving code-switched emotion detection. We first consider a bilingual-view parallel translation for code-switched text enhancement, i.e., translating the code-switched texts into two languages. Then we propose an adversarial dual-channel encoder architecture, where two private encoders take as inputs the parallel texts in two languages, respectively. The private encoders and the shared encoder work collaboratively, and effectively retrieve the features from monolingual and bilingual perspectives under adversarial training. We conduct extensive experiments on five code-switched benchmark datasets. Results show that our model outperforms the strongly-performing baselines that leverage external code-switched or bilingual word embedding with over 1.5% F1 score on the Chinese–English, Spanish–English and Hindi–English code-mixed data, becoming the new state-of-the-art system. Further analyses including ablation, qualitative and error studies, demonstrate the effectiveness of our proposed encoder for code-switched texts, as well as the bilingual-view parallel translation strategy.

代码转换情感检测是一项分析代码转换文本中情感的任务，近年来受到越来越多的研究关注。先前的工作利用具有复杂特征的各种神经模型来追求任务的高性能，但他们仍然忽略了代码切换文本的一些关键特征。在这项工作中，我们提出了一种改进代码切换情绪检测的创新方法。我们首先考虑用于代码切换文本增强的双语视图并行翻译，即将代码切换文本翻译成两种语言。然后我们提出了一种对抗性双通道编码器架构，其中两个私有编码器分别将两种语言的并行文本作为输入。私有编码器和共享编码器协同工作，并在对抗训练下从单语和双语的角度有效地检索特征。我们对五个代码切换基准数据集进行了广泛的实验。结果表明，我们的模型优于利用外部代码切换或双语词嵌入的性能强劲的基线，在中文-英文、西班牙文-英文和印地文-英文代码混合数据上的 F1 分数超过 1.5%，成为新的状态-最先进的系统。包括消融、定性和错误研究在内的进一步分析证明了我们提出的编码器对代码切换文本以及双语视图平行翻译策略的有效性。结果表明，我们的模型优于利用外部代码切换或双语词嵌入的性能强劲的基线，在中文-英文、西班牙文-英文和印地文-英文代码混合数据上的 F1 分数超过 1.5%，成为新的状态-最先进的系统。包括消融、定性和错误研究在内的进一步分析证明了我们提出的编码器对代码切换文本以及双语视图平行翻译策略的有效性。结果表明，我们的模型优于利用外部代码切换或双语词嵌入的性能强劲的基线，在中文-英文、西班牙文-英文和印地文-英文代码混合数据上的 F1 分数超过 1.5%，成为新的状态-最先进的系统。包括消融、定性和错误研究在内的进一步分析证明了我们提出的编码器对代码切换文本以及双语视图平行翻译策略的有效性。