Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence relation extraction, finding that with names or synonyms as targets, their textual semantics and correlation (in terms word pattern) among them affect model performance. We then propose ...

Open Relation Extraction (ORE) task remains a challenge to obtain semantic representation by discovering arbitrary relations from the unstructured text. Conventional methods heavily depend on feature engineering or syntactic parsing, which are inefficient error-cascading. Recently, leveraging supervised deep learning address ORE is promising way. However, there two main challenges: (1) The lack...