Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
复制标题
DOI：
10.18653/v1/s19-1015
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Xiaolei Huang;Michael J. Paul
中科院分区：
文献类型：
--
作者：
Xiaolei Huang;Michael J. Paul
语言的使用因不同的人口统计因素（例如性别、年龄和地理位置）而异。然而，大多数现有的文档分类方法忽略了人口统计变化。在本研究中，我们根据经验研究了文本数据如何随着四个人口统计因素的变化而变化：性别、年龄、国家和地区。我们提出了一种多任务神经模型，通过对抗性训练来解释人口变化。在四个英语社交媒体数据集的实验中，我们发现在适应用户因素时分类性能会提高。
Language use varies across different demographic factors, such as gender, age, and geographic location. However, most existing document classification methods ignore demographic variability. In this study, we examine empirically how text data can vary across four demographic factors: gender, age, country, and region. We propose a multitask neural model to account for demographic variations via adversarial training. In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.