PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction
This addresses the inefficiency in utilizing bag-level data for relation extraction, offering a simple yet effective solution for researchers and practitioners in natural language processing.
The paper tackles the problem of distantly supervised relation extraction (DS-RE) by proposing PARE, a baseline method that concatenates sentences into passages and encodes them jointly with BERT, outperforming existing state-of-the-art models in monolingual and multilingual datasets.
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.