We put forward an end-to-end multi-step machine learning model which jointly labels semantic roles and the proto-roles of Dowty (1991), given a sentence and the predicates therein. Our best architecture first learns argument spans followed by learning the argument's syntactic heads. This information is shared with the next steps for predicting the semantic roles and proto-roles. We also experiment with transfer learning from argument and head prediction to role and proto-role labeling. We compare using static and contextual embeddings for words, arguments, and sentences. Unlike previous work, our model does not require pre-training or fine-tuning on additional tasks, beyond using off-the-shelf (static or contextual) embeddings and supervision. It also does not require argument spans, their semantic roles, and/or their gold syntactic heads as additional input, because it learns to predict all these during training. Our multi-task learning model raises the state-of-the-art predictions for most proto-roles.

本文提出一种端到端的多步骤机器学习模型，可以联合标记语义角色和Dowty（1991）的原型角色，其最佳架构首先学习参数跨度，然后学习参数的句法头，从而预测语义角色和原型角色，通过在训练期间预测所有这些，使用静态或上下文嵌入和监督完成所有任务，从而提高了大多数原型角色的最新预测。

联合语义角色和原型角色标注的多任务学习