  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.
