Simple BERT Models for Relation Extraction and Semantic Role Labeling (1904.05255v1)

Abstract: We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple BERT-based model can achieve state-of-the-art performance. To our knowledge, we are the first to successfully apply BERT in this manner. Our models provide strong baselines for future research.

• The paper introduces a BERT-based relation extraction model that reached a 67.8% F1 score on the TACRED benchmark without using additional lexical or syntactic features.
• The paper presents a unified BERT framework for semantic role labeling, achieving a 90.3% F1 score on the CoNLL 2009 dataset for both argument identification and classification.
• The paper shows that simplified neural architectures powered by BERT embeddings can forgo complex hand-crafted features while maintaining competitive performance.

An Overview of Simple BERT Models for Relation Extraction and Semantic Role Labeling

The paper "Simple BERT Models for Relation Extraction and Semantic Role Labeling" by Peng Shi and Jimmy Lin explores the application of Bidirectional Encoder Representations from Transformers (BERT) for relation extraction and semantic role labeling (SRL). The authors argue that pre-trained BERT models can achieve state-of-the-art performance on these tasks without incorporating any additional lexical or syntactic features, challenging the prevalent reliance on such features in existing models. This paper positions simple BERT-based models as strong baselines for future research initiatives in natural language understanding.

Core Contributions

1. Relation Extraction Without External Features: The paper introduces a BERT-based model for relation extraction, bypassing the use of external lexical and syntactic features typically deemed necessary for achieving competitive performance. Through extensive evaluation on the TACRED benchmark, the model demonstrates comparable or superior performance against prior methods that incorporate Graph Convolutional Networks (GCNs) and other syntactic tree encodings. Specifically, the BERT-LSTM-base model achieves an $F_1$ score of 67.8%, outperforming several previous approaches.
2. Semantic Role Labeling Using BERT: For semantic role labeling, the authors propose a model that unifies both span-based and dependency-based SRL within the BERT framework. This model is evaluated on distinguished SRL benchmarks such as CoNLL 2005, CoNLL 2009, and CoNLL 2012 datasets, observing significant improvements in $F_1$ scores across these tasks. The BERT-LSTM-large configuration records an $F_1$ score of 90.3% for argument identification and classification in the CoNLL 2009 test set.
3. Simplified Neural Architectures: A notable takeaway from this work is the demonstration that simplified neural architectures benefiting from BERT embeddings can mitigate the necessity of hand-crafted syntactic features and constraints for relation extraction and semantic role labeling. This reduction in complexity does not compromise performance, pointing to the capacity of BERT to inherently capture the intricate relationships and roles that were traditionally addressed through syntactic augmentation.

Numerical Results and Comparative Analysis

The paper presents comprehensive experimental results, emphasizing the performance of simple BERT-based models on core NLP tasks without feature engineering. For instance, the relation extraction model reports an improved BERT-LSTM-base result over sophisticated graph-based models despite its simplistic design. Likewise, the semantic role labeling approach outshines contemporary systems, establishing a new benchmark for predicate disambiguation and argument classification tasks.

Implications and Future Directions

The outcomes evidenced by Shi and Lin elicit several implications for the NLP research community. Practically, the work underscores the efficiency of pre-trained LLMs like BERT in meeting the needs of relation extraction and SRL tasks without the dependency on syntactic resources, enabling more language-agnostic applications. Theoretically, this research invites speculation on enhancing BERT-based models further through multi-task learning approaches or the selective reintroduction of syntactic features to augment performance.

Potential future research avenues could explore personalized model configurations, adaptive fine-tuning techniques for various datasets, or extending this methodology to other NLP tasks. Such explorations would complement the results shown here, reinforcing the utility of BERT in versatile, low-resource language processing contexts. The foundational insights from this paper indeed reinforce the trajectory towards streamlined models leveraging robust language representations to tackle complex linguistic phenomena.