Relational structure extraction covers a wide range of tasks and plays an
important role in natural language processing. Recently, many approaches tend
to design sophisticated graphical models to capture the complex relations
between objects that are described in a sentence. In this work, we demonstrate
that simple tagging models can surprisingly achieve competitive performances
with a small trick -- priming. Tagging models with priming append information
about the operated objects to the input sequence of pretrained language model.
Making use of the contextualized nature of pretrained language model, the
priming approach help the contextualized representation of the sentence better
embed the information about the operated objects, hence, becomes more suitable
for addressing relational structure extraction. We conduct extensive
experiments on three different tasks that span ten datasets across five
different languages, and show that our model is a general and effective model,
despite its simplicity. We further carry out comprehensive analysis to
understand our model and propose an efficient approximation to our method,
which can perform almost the same performance but with faster inference speed.

通过使用 Tagging 模型，对预先训练好的语言模型的输入序列增加预处理，从而提高关联结构提取的精度，并在多语言数据集上进行了广泛的实验及分析，并提出了一种快速且准确的近似方法

一个简单且统一的标记模型，带有引导作用，用于关系结构预测

A Simple and Unified Tagging Model with Priming for Relational Structure Predictions

Keyphrases efficiently summarize a document's content and are used in various
document processing and retrieval tasks. Several unsupervised techniques and
classifiers exist for extracting keyphrases from text documents. Most of these
methods operate at a phrase-level and rely on part-of-speech (POS) filters for
candidate phrase generation. In addition, they do not directly handle
keyphrases of varying lengths. We overcome these modeling shortcomings by
addressing keyphrase extraction as a sequential labeling task in this paper. We
explore a basic set of features commonly used in NLP tasks as well as
predictions from various unsupervised methods to train our taggers. In addition
to a more natural modeling for the keyphrase extraction problem, we show that
tagging models yield significant performance benefits over existing
state-of-the-art extraction methods.

本文提出了一种序列标注方法，将关键短语提取问题变为一个更自然的建模方式，并表明标注模型相比现有最先进的提取方法具有显著的性能优势。