TL;DR本研究旨在解决图形预测的集成问题,作者提出了一个高效的启发式算法来近似最优解,在 AMR 解析问题中实验结果表明该算法比单个模型更加精准且更具鲁棒性。
Abstract
In many machine learning tasks, models are trained to predict structure data
such as graphs. For example, in natural language processing, it is very common
to parse texts into dependency trees or abstract meaning representation (AMR)
graphs. On the other hand, ensemble methods combine