Do unsupervised methods for learning rich, contextualized token
representations obviate the need for explicit modeling of linguistic structure
in neural network models for semantic role labeling (SRL)? We address this
question by incorporating the massively successful ELMo embeddings (Peters et
al., 2018) into LISA (Strubell et al., 2018), a strong, linguistically-informed
neural network architecture for SRL. In experiments on the CoNLL-2005 shared
task we find that though ELMo out-performs typical word embeddings, beginning
to close the gap in F1 between LISA with predicted and gold syntactic parses,
syntactically-informed models still out-perform syntax-free models when both
use ELMo, especially on out-of-domain data. Our results suggest that linguistic
structures are indeed still relevant in this golden age of deep learning for
NLP.

本研究研究了关于是否通过无监督的方式来学习丰富的上下文化令牌表示，从而消除了在神经网络模型中显式建模语言结构的需要，最后得出结论，即使使用 ELMo 表示，句法相关模型仍然优于无句法模型，尤其是对于领域外的数据。