The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use the multi-task learning mechanism to integrate the trigger features into the event extraction model. For the document-level event extraction subtask, we design an Encoder-Decoder based method and propose a Transformer-alike decoder. Finally,our system ranks No.4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79.887%, 85.179%, and 70.828% respectively. The codes of our model are available at {https://github.com/neukg/MultiIE}.
翻译:2021年语言和情报挑战中的多格式信息提取任务旨在全面评估不同维度的信息提取任务。 它由多个空档关系提取子任务和两个事件提取子任务组成, 从句级和文档级中提取事件。 我们在这里描述我们的多格式信息提取竞争任务系统。 具体来说, 我们将其转换为传统的三重提取任务, 并设计一个基于投票的方法, 充分利用现有模型。 对于判决级事件提取子任务, 我们将其转换为 NER 任务, 并使用基于点标签的方法进行提取。 此外, 考虑附加说明的触发信息可能有助于事件提取, 我们设计了一个辅助触发识别模型, 并使用多任务学习机制将触发特性纳入事件提取模式。 对于文件级提取子任务, 我们设计了一个基于传统三重提取任务的 Encoder-Decoder 方法, 并提议一个类似变压器的解码。 最后, 我们的系统在这项多格式信息提取任务测试板上排名第4号, 使用基于点标签的标签方法 。 此外, 我们的 F1 触发信息提取% mx 级别文件的排序为 。