Many tasks in natural language processing require the extraction of relationship information for a given condition, such as event argument extraction, relation extraction, and task-oriented semantic parsing. Recent works usually propose sophisticated models for each task independently and pay less attention to the commonality of these tasks and to have a unified framework for all the tasks. In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems. TAGPRIME is a sequence tagging model that appends priming words about the information of the given condition (such as an event trigger) to the input text. With the self-attention mechanism in pre-trained language models, the priming words make the output contextualized representations contain more information about the given condition, and hence become more suitable for extracting specific relationships for the condition. Extensive experiments and analyses on three different tasks that cover ten datasets across five different languages demonstrate the generality and effectiveness of TAGPRIME.

翻译：自然语言处理中的许多任务需要针对给定条件提取关系信息，例如事件论元抽取、关系抽取和任务导向的语义解析。近期工作通常针对各任务独立设计复杂模型，且较少关注这些任务的共性以及构建统一框架。本文提出以统一视角审视所有此类任务，并引入TAGPRIME来解决关系结构提取问题。TAGPRIME是一种序列标注模型，它将关于给定条件（如事件触发词）信息的启动词附加到输入文本中。借助预训练语言模型中的自注意力机制，启动词使得输出的上下文表示包含更多关于给定条件的信息，从而更适用于提取该条件对应的特定关系。在涵盖五种语言的十个数据集上，针对三个不同任务的广泛实验与分析证明了TAGPRIME的通用性和有效性。