A Case Study on Learning a Unified Encoder of Relations
Lisheng Fu, Bonan Min, Thien Huu Nguyen, Ralph Grishman · 2018
Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema.An individual corpus is often small, and the models may often be biased or overfitted to the corpus.We hypothesize that we can learn a better representation by combining multiple relation datasets.We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training.The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different.We use ACE05 and ERE datasets as our case study for experiments.The multi-task model obtains significant improvement on both datasets.