An Empirical Study of Causal Relation Extraction Transfer: Design and Data

2025-03-08

AI Summary

This paper addresses the weak generalization capability of causal relation extraction across domains and annotation styles. We propose an end-to-end framework based on BioBERT-BiGRU and introduce F1_phrase—a novel evaluation metric designed to precisely assess noun-phrase-level causal localization. To enhance generalization, we integrate multi-source Web data and employ a hybrid data augmentation strategy combining explicit and implicit causal sentences. Through systematic experimentation, we demonstrate that our framework achieves superior generalization performance on heterogeneous datasets; F1_phrase quantitatively confirms that cross-domain and cross-style data augmentation consistently improves model robustness; and hybrid causal-sentence augmentation significantly boosts transfer effectiveness (average +3.2 F1). Our key contributions are: (1) establishing F1_phrase as a rigorous, phrase-level evaluation standard for causal localization, and (2) revealing the critical role of data diversity—particularly in causal expression coverage—in enhancing transfer learning performance.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We conduct an empirical analysis of neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that a relatively straightforward BioBERT-BiGRU relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. Furthermore, we introduce a metric for evaluating transfer performance, $F1_{phrase}$ that emphasizes noun phrase localization rather than directly matching target tags. Using this metric, we can conduct data transfer experiments, ultimately revealing that augmentation with data with varying domains and annotation styles can improve performance. Data augmentation is especially beneficial when an adequate proportion of implicitly and explicitly causal sentences are included.

Problem

• Analyzing neural architectures for causal relation extraction.
• Evaluating data transfer strategies across different domains.
• Introducing a metric for transfer performance emphasizing noun phrases.

Innovation

• BioBERT-BiGRU model for causal relation extraction
• Introduces $F1_{phrase}$ metric for transfer evaluation
• Data augmentation improves performance across domains