  In practical scenario, relation extraction needs to first identify entity
pairs that have relation and then assign a correct relation class. However, the
number of non-relation entity pairs in context (negative instances) usually far
exceeds the others (positive instances), which negatively affects a model's
performance. To mitigate this problem, we propose a multi-task architecture
which jointly trains a model to perform relation identification with
cross-entropy loss and relation classification with ranking loss. Meanwhile, we
observe that a sentence may have multiple entities and relation mentions, and
the patterns in which the entities appear in a sentence may contain useful
semantic information that can be utilized to distinguish between positive and
negative instances. Thus we further incorporate the embeddings of
character-wise/word-wise BIO tag from the named entity recognition task into
character/word embeddings to enrich the input representation. Experiment
results show that our proposed approach can significantly improve the
performance of a baseline model with more than 10% absolute increase in
F1-score, and outperform the state-of-the-art models on ACE 2005 Chinese and
English corpus. Moreover, BIO tag embeddings are particularly effective and can
be used to improve other models as well.
