We explore a novel approach for Semantic Role Labeling (SRL) by casting it as
a sequence-to-sequence process. We employ an attention-based model enriched
with a copying mechanism to ensure faithful regeneration of the input sequence,
while enabling interleaved generation of argument role labels. Here, we apply
this model in a monolingual setting, performing PropBank SRL on English
language data. The constrained sequence generation set-up enforced with the
copying mechanism allows us to analyze the performance and special properties
of the model on manually labeled data and benchmarking against state-of-the-art
sequence labeling models. We show that our model is able to solve the SRL
argument labeling task on English data, yet further structural decoding
constraints will need to be added to make the model truly competitive. Our work
represents a first step towards more advanced, generative SRL labeling setups.

本文探讨了一种将语义角色标注作为序列到序列过程的新方法，使用加强的基于注意力机制和复制机制的模型，对英文数据进行 PropBank SRL，证明该模型可以解决英语数据上的 SRL 标注任务，但需要添加更多的结构解码约束来使该模型真正具有竞争力。