论文标题
用变压器语言模型将零拍的社会政治文本排名以减少紧密的阅读时间
Zero-Shot Ranking Socio-Political Texts with Transformer Language Models to Reduce Close Reading Time
论文作者
论文摘要
我们将分类问题作为一个综合问题，并将零射门排名应用于社会政治文本。排名最高的文档可以视为正面分类的文档，这减少了信息提取过程的近乎阅读时间。我们使用变压器语言模型来获取需要概率并研究不同类型的查询。我们发现，Deberta的平均平均精度比Roberta的平均平均精度得分高，并且当使用类标签的声明形式用作查询时，它的表现优于类标签的字典定义。我们表明，可以通过获取一百分比的排名文档来减少近距离阅读时间，而这些文档的百分比取决于他们想要实现的次数。但是，我们的发现还表明，随着主题越来越广泛，应读取的文档的百分比会增加。
We approach the classification problem as an entailment problem and apply zero-shot ranking to socio-political texts. Documents that are ranked at the top can be considered positively classified documents and this reduces the close reading time for the information extraction process. We use Transformer Language Models to get the entailment probabilities and investigate different types of queries. We find that DeBERTa achieves higher mean average precision scores than RoBERTa and when declarative form of the class label is used as a query, it outperforms dictionary definition of the class label. We show that one can reduce the close reading time by taking some percentage of the ranked documents that the percentage depends on how much recall they want to achieve. However, our findings also show that percentage of the documents that should be read increases as the topic gets broader.
