双语：靠Emojis就能分辨人的情绪?!

　　Emoji icons have helped scientists train computers to understand sarcasm.

　　表情符号有助于科学家研究开发电脑在理解讽刺情绪上的功能。

　　Researchers used 1.2 billion tweets containing at least one of the most popular 64 emojis to develop DeepMoji.

　　研究人员收集了12亿条推文，每条推文都至少有64种最受欢迎表情符号中的一个，这也是为了开发DeepMoji。

　　The algorithm learned first to predict which emoji was likely to be used and then to recognise sarcasm, allowing it to spot hate speech faster than humans.

　　该算法首先学会了预测哪些表情可能被使用，然后识别言语中的讽刺情绪，让它能比人类更快地发现言论中的。

　　It could also be used to assess how people really feel about different brands or to improve human interaction with smart programs such as chatbots.

　　它还可以用来评估人们对不同品牌的真实感受，或者改进与智能程序(如聊天机器人)的人际互动。

　　"Because we can’t use intonation in our voice or body language to contextualise what we are saying, emojis are the way we do it online," Prof Iyad Rahwan told MIT’s Technology Review magazine.

　　“因为网络聊天的时候我们不用我们的声音或肢体语言来表达我们的情绪，我们在网络上使用的是表情符号，”Iyad Rahwan教授告诉麻省理工学院的技术评论杂志。

　　Prof Iyad Rahwan and graduate student Bjarke Felbo have created a website inviting people to refine DeepMoji’s education by submitting and annotating their own messages.

　　Iyad Rahwan教授和研究生Bjarke Felbo创建了一个网站，邀请人们通过提交和注释自己的信息(中的情绪)来改进DeepMoji的识别。

　　The pair also plan to release the algorithm’s code so it can be used by other researchers.

　　他们还计划公布算法的代码，以便其他研究人员使用。

　　"Using emojis as labels for training neural networks is a great idea," said Prof Kerstin Dautenhahn who studies human-machine interaction at the University of Hertfordshire.

　　“使用表情符号作为训练网络的识别能力是一个伟大的想法，”在赫特福德大学研究人机交互的Kerstin Dautenhahn教授说。

　　"Applying it to tweets seems also a smart choice, since communication via tweets is much more impoverished than actual face-to-face conversation," she said, "so chances are better for the algorithms to work."

　　她说:“推特里的用这些表情符号也是一个明智的选择，因为通过文字进行交流比面对面的交流困难。所以，有这样的算法使得系统识别的可能性更大。”

　　Prof Dautenhahn raised doubts about the wide range of human activity that was now being monitored and analysed to try to guess what people think and feel.

　　Dautenhahn教授对目前这些对人类活动对监测和分析来试图猜测人们的想法和感受提出了质疑。