论文标题
从验证的语言模型中构建分类学
Constructing Taxonomies from Pretrained Language Models
论文作者
论文摘要
我们提出了一种使用验证的语言模型来构建分类树（例如WordNet）的方法。我们的方法由两个模块组成，一个模块可以预测育儿关系，另一种将这些预测调解为树木的模块。父母的预测模块为每个潜在的亲子对产生似然得分，创建了亲子关系分数的图。树对帐模块将任务视为图形优化问题，并输出此图的最大生成树。我们在从WordNet采样的子树上训练模型，并对非重叠的WordNet子树进行测试。我们表明，结合网络重新覆盖可以进一步提高性能。关于构建英语WordNet子树的任务，该模型达到了66.7祖先F1，比以前的最佳发表结果相对增长了20.0％。此外，我们使用开放的多语言WordNet将原始英语数据集转换为其他九种语言，并将结果扩展到这些语言中。
We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on non-overlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.
