Open Information Extraction (OIE) is a structured prediction (SP) task in
Natural Language Processing (NLP) that aims to extract structured $n$-ary
tuples - usually subject-relation-object triples - from free text. The word
embeddings in the input text can be enhanced with linguistic features, usually
Part-of-Speech (PoS) and Syntactic Dependency Parse (SynDP) labels. However,
past enhancement techniques cannot leverage the power of pretrained language
models (PLMs), which themselves have been hardly used for OIE. To bridge this
gap, we are the first to leverage linguistic features with a Seq2Seq PLM for
OIE. We do so by introducing two methods - Weighted Addition and Linearized
Concatenation. Our work can give any neural OIE architecture the key
performance boost from both PLMs and linguistic features in one go. In our
settings, this shows wide improvements of up to 24.9%, 27.3% and 14.9% on
Precision, Recall and F1 scores respectively over the baseline. Beyond this, we
address other important challenges in the field: to reduce compute overheads
with the features, we are the first ones to exploit Semantic Dependency Parse
(SemDP) tags; to address flaws in current datasets, we create a clean synthetic
dataset; finally, we contribute the first known study of OIE behaviour in SP
models.

自然语言处理中的开放信息提取（OIE）通过引入预训练语言模型和语言特征，通过 Seq2Seq 预训练语言模型提升 OIE 架构性能，并在性能评估中获得了 24.9%、27.3% 和 14.9% 的改进。此外，还介绍了其他挑战，如利用语义依赖解析标签减少计算开销、创建干净的合成数据集以及探究 OIE 行为在结构化预测模型中的表现。