A relation tuple consists of two entities and the relation between them, and
often such tuples are found in unstructured text. There may be multiple
relation tuples present in a text and they may share one or both entities among
them. Extracting such relation tuples from a sentence is a difficult task and
sharing of entities or overlapping entities among the tuples makes it more
challenging. Most prior work adopted a pipeline approach where entities were
identified first followed by finding the relations among them, thus missing the
interaction among the relation tuples in a sentence. In this paper, we propose
two approaches to use encoder-decoder architecture for jointly extracting
entities and relations. In the first approach, we propose a representation
scheme for relation tuples which enables the decoder to generate one word at a
time like machine translation models and still finds all the tuples present in
a sentence with full entity names of different length and with overlapping
entities. Next, we propose a pointer network-based decoding approach where an
entire tuple is generated at every time step. Experiments on the publicly
available New York Times corpus show that our proposed approaches outperform
previous work and achieve significantly higher F1 scores.

本文提出了两种使用编码器 - 解码器结构的方法来共同提取关系元组和实体，其中第一种方法采用关系元组表示方案，使解码器能够像机器翻译模型一样逐个单词生成，并仍然找到具有不同长度和重叠实体的所有元组，而第二种方法采用指针网络解码方法，每个时间步长生成整个元组。实验结果表明，本文提出的方法优于先前的工作，并实现了显着更高的 F1 分数。