  Current state-of-the-art relation extraction methods typically rely on a set
of lexical, syntactic, and semantic features, explicitly computed in a
pre-processing step. Training feature extraction models requires additional
annotated language resources, which severely restricts the applicability and
portability of relation extraction to novel languages. Similarly,
pre-processing introduces an additional source of error. To address these
limitations, we introduce TRE, a Transformer for Relation Extraction, extending
the OpenAI Generative Pre-trained Transformer [Radford et al., 2018]. Unlike
previous relation extraction models, TRE uses pre-trained deep language
representations instead of explicit linguistic features to inform the relation
classification and combines it with the self-attentive Transformer architecture
to effectively model long-range dependencies between entity mentions. TRE
allows us to learn implicit linguistic features solely from plain text corpora
by unsupervised pre-training, before fine-tuning the learned language
representations on the relation extraction task. TRE obtains a new
state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets,
achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a
significant increase in sample efficiency. With only 20% of the training
examples, TRE matches the performance of our baselines and our model trained
from scratch on 100% of the TACRED dataset. We open-source our trained models,
experiments, and source code.
