Crossing the Line: Where do Demographic Variables Fit into Humor Detection?
Crossing the Line: Where do Demographic Variables Fit into Humor Detection?
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跨越界限：人口统计变量在幽默检测中有何作用？
DOI：
10.18653/v1/2020.acl-srw.24
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发表时间：
2020
期刊：
影响因子：
0.2
通讯作者：
J. A Meaney
中科院分区：
文献类型：
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作者：
J. A Meaney
最近的幽默分类共享的任务在两个问题上挣扎：数据包括高度约束的幽默类型，并不代表幽默，或者数据是如此不加区分，以至于对其幽默内容的跨性别者一致尽管幽默是一种高度主观的现象，但通常在所有注释者的法官中的平均水平。我们认为，我们提出了有关幽默注释者的人口统计信息，以更加明智地汇总对于更细微的共享任务，并且可能会在下游任务（例如内容审核）上提高性能。
Recent humor classification shared tasks have struggled with two issues: either the data comprises a highly constrained genre of humor which does not broadly represent humor, or the data is so indiscriminate that the inter-annotator agreement on its humor content is drastically low. These tasks typically average over all annotators’ judgments, in spite of the fact that humor is a highly subjective phenomenon. We argue that demographic factors influence whether a text is perceived as humorous or not. We propose the addition of demographic information about the humor annotators in order to bin ratings more sensibly. We also suggest the addition of an ‘offensive’ label to distinguish between different generations, in terms of humor. This would allow for more nuanced shared tasks and could lead to better performance on downstream tasks, such as content moderation.