End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models

TL;DR

This paper introduces an end-to-end neural model that jointly performs named entity recognition and relation extraction without external NLP tools, leveraging pre-trained language models for improved speed and performance across multiple domains.

Contribution

The paper presents a novel end-to-end neural approach that integrates pre-trained language models for joint NER and RE, avoiding reliance on external NLP tools and enhancing efficiency.

Findings

Achieves state-of-the-art or competitive results on 5 datasets.

Does not depend on external NLP tools like dependency parsers.

Fast training due to pre-trained parameters and self-attention architecture.

Abstract

Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \& IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.