We present an overview of the EmotionX 2019 Challenge, held at the 7th
International Workshop on Natural Language Processing for Social Media
(SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting
emotions in spoken and chat-based dialogues using augmented EmotionLines
datasets. EmotionLines contains two distinct datasets: the first includes
excerpts from a US-based TV sitcom episode scripts (Friends) and the second
contains online chats (EmotionPush). A total of thirty-six teams registered to
participate in the challenge. Eleven of the teams successfully submitted their
predictions performance evaluation. The top-scoring team achieved a micro-F1
score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the
chat-based dialogues (EmotionPush).

本文介绍了 2019 年 EmotionX 挑战活动，该挑战活动在第七届自然语言处理社交媒体研讨会（SocialNLP）上进行。EmotionX 基于 EmotionLines 数据集，旨在预测口语和聊天式对话中的情感。在本次挑战活动中，有 36 个团队参加，其中 11 个团队成功提交了预测，并且得分最高的团队在口语和聊天式对话中的表现均达到了 81.5％和 79.5％。

SocialNLP EmotionX 2019 挑战概述：预测口语和聊天中的情感

SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in  Spoken Dialogues and Chats

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.

本文介绍了 EmotionLines 数据集，这是第一个只基于文本内容在每个对话中为所有话语进行情感标注的数据集，共包括来自 Friends 电视剧和私人 Facebook 信使对话的 2000 个对话中的 29245 种话语，并为 EmotionLines 中的情感检测模型提供了多个强基线。