A Frustratingly Easy Approach for Entity and Relation Extraction (2010.12812v2)

Abstract: End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16$\times$ speedup with a slight reduction in accuracy.

A Frustratingly Easy Approach for Entity and Relation Extraction

In the domain of information extraction, the task of identifying entities and their interrelations within text data is pivotal. Traditionally approached through joint models, the landscape of entity and relation extraction is re-evaluated in this paper with a perspective that emphasizes simplicity and efficiency. Presented by Zexuan Zhong and Danqi Chen, this work introduces a pipelined methodology that departs from joint structured models to accomplish state-of-the-art results using standard benchmarks like ACE04, ACE05, and SciERC.

The crux of this approach lies in the paradigm shift from joint modeling to independent learning of entities and relations through two separate encoders. The entity model is tasked with named entity recognition, generating span-level representations, while the relation model processes pairs of entities to ascertain relational context. Critical to this model's superiority is the utilization of contextual representations tailored specifically for each span pair, discarding the often counterproductive shared representations in joint models.

Empirical results illustrate an absolute performance enhancement in relation F1 scores by 1.7%-2.8% over prior models utilizing the same pre-trained encoders. This underscores the significance of distinct contextual modeling for different tasks. The integration of cross-sentence context is shown to be advantageous, especially in managing long-range dependencies, thereby bolstering both entity and relation extractions.

Practically, this model’s simplicity translates into an efficient pipeline with a robust approximation strategy that cuts inference time by 8-16 times, while only incurring a minuscule accuracy trade-off. This makes the approach not only computationally appealing but also practical for real-world applications where speed is of essence alongside accuracy.

The implications of this research challenge the conventionally held belief in the indispensability of joint modeling for effective information extraction. It provokes a rethinking of how entity and relation extraction systems are architectured, propelling forward a paradigm where independent task modeling can yield superior results.

Looking towards future directions, this methodology could influence further advancements in AI, particularly in low-resource settings where data scarcity imposes constraints on traditional complex models. Additionally, extending this model to encompass multilingual capabilities or adapting it to new domains could serve as fertile ground for exploration.

In conclusion, this paper makes a compelling case for re-examining the dynamics of entity and relation extraction. By prioritizing task-specific pipeline strategies over joint models, it sets a clear precedent for future research and application in the field.