In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fined-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performances. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a test micro-F1 of 0.7463.
翻译:在本文中,我们在SemEval-2019的文本对话中介绍了我们关于情感检测任务的模型。我们的模型通过使用外部微调的字表和DeepMoji句表解,扩展了经常性的革命神经网络(RCNN),我们还探讨了其他一些经过培训的有竞争力的字表和句表,包括ELMO、BERT和InferSent,但发现性能较差。此外,我们进行了广泛的敏感性分析,从经验上表明,我们的模型对超参数比较强。我们的模型不需要手工制作的特征或情感精密,但用0.7463的微-F1测试取得了良好的性能。