论文标题
五个心理语言特征，可更好地与用户互动
Five Psycholinguistic Characteristics for Better Interaction with Users
论文作者
论文摘要
当两个人相互关注并且对对方所说或写的话感兴趣时，他们几乎立即适应了写作/说话风格以匹配对方。要成功与用户进行互动，聊天机器人和对话系统应该能够做到这一点。我们提出了一个框架，该框架由五个心理语言文本特征组成，可更好地与人类计算机的互动。我们描述用于收集数据的注释过程，并基准测试五个二进制分类任务，以尝试不同的训练大小和模型体系结构。最佳体系结构明显胜过几个基线，并取决于语言和任务，达到72 \％和96 \％之间的宏观平均f $ _1 $ - 分数。事实证明，即使使用正确的体系结构，即使使用少量的手动注释数据，也很容易为各种语言建模。
When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful interaction with a user, chatbots and dialogue systems should be able to do the same. We propose a framework consisting of five psycholinguistic textual characteristics for better human-computer interaction. We describe the annotation processes used for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. The best architectures noticeably outperform several baselines and achieve macro-averaged F$_1$-scores between 72\% and 96\% depending on the language and the task. The proposed framework proved to be fairly easy to model for various languages even with small amount of manually annotated data if right architectures are used.
