  Neural models for distantly supervised relation extraction (DS-RE) encode
each sentence in an entity-pair bag separately. These are then aggregated for
bag-level relation prediction. Since, at encoding time, these approaches do not
allow information to flow from other sentences in the bag, we believe that they
do not utilize the available bag data to the fullest. In response, we explore a
simple baseline approach (PARE) in which all sentences of a bag are
concatenated into a passage of sentences, and encoded jointly using BERT. The
contextual embeddings of tokens are aggregated using attention with the
candidate relation as query -- this summary of whole passage predicts the
candidate relation. We find that our simple baseline solution outperforms
existing state-of-the-art DS-RE models in both monolingual and multilingual
DS-RE datasets.
