Feeling emotion is a critical characteristic to distinguish people from machines. Among all the multi-modal resources for emotion detection, textual datasets are those containing the least additional information in addition to semantics, and hence are adopted widely for testing the developed systems. However, most of the textual emotional datasets consist of emotion labels of only individual words, sentences or documents, which makes it challenging to discuss the contextual flow of emotions. In this paper, we introduce EmotionLines, the first dataset with emotions labeling on all utterances in each dialogue only based on their textual content. Dialogues in EmotionLines are collected from Friends TV scripts and private Facebook messenger dialogues. Then one of seven emotions, six Ekman's basic emotions plus the neutral emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245 utterances from 2,000 dialogues are labeled in EmotionLines. We also provide several strong baselines for emotion detection models on EmotionLines in this paper.
翻译：情感情感是区分人和机器的关键特征。在所有情感检测的多模式资源中,文本数据集是除了语义外包含最少的额外信息的数据集,因此被广泛采用以测试发达的系统。然而,大部分文字情感数据集由仅单词、句子或文件的情感标签组成,这使得讨论情感背景流动具有挑战性。在本文中,我们引入了情感线,即每场对话中第一个带有情感标签的情感数据集,每个语句仅以文字内容为基础。情感线中的对话是从朋友电视脚本和私人脸书信使对话中收集的。然后,7种情感中的一种,6种埃克曼的基本情感加上中立情感,由5个亚马逊·图尔克尔斯在每段的言语上贴上标签。总共2 000个对话中的29 245个话在情感流中贴上标签。我们还提供了几条强烈的基线,用于本文中情感线的情感检测模型。
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