基于转换的深度输入线性化
Transition-Based Deep Input Linearization
摘要：
传统的深度NLG方法采用流水线方法，包括构造句法输入、预测功能词、线性化句法输入和生成表面形式等阶段。虽然更容易可视化，但流水线方法遭受错误传播。此外，各模块之间的现有信息不能为所有模块所利用。我们构建了一个过渡为基础的模型，共同执行线性化，功能词预测和形态生成，这大大提高了精度相比，一个流水线基线系统。在一个标准的深度输入线性化共享任务，我们的系统实现了迄今为止报道的最佳结果。
英文摘要：
Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
