[Paper Review] A Structured Learning Approach to Temporal Relation Extraction

Ning Qiang, Zhili Feng+1 more|arXiv (Cornell University)|Jun 12, 2019

Natural Language Processing Techniques41 references4 citations

TL;DR

This paper proposes a structured learning approach for temporal relation extraction that jointly models global dependencies among events, improving performance over local methods by enforcing transitivity and symmetry constraints. By integrating unlabeled data and refining predictions through iterative global inference, the method achieves state-of-the-art F1 scores of 48.53 on TD-Test and a 14.8% relative improvement over ClearTK on TE3-PT.

ABSTRACT

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.

Motivation & Objective

• Address the challenge of temporal relation extraction in natural language, where local models often fail due to global inconsistencies like transitivity and symmetry violations.
• Overcome the limitations of existing local models that make independent pairwise decisions without considering the full temporal graph structure.
• Handle missing relations in annotations by leveraging structured learning to infer plausible relations through global consistency constraints.
• Utilize unlabeled data effectively through a semi-supervised structured learning framework to improve generalization.
• Demonstrate superior performance on both sparsely and densely annotated datasets, showing robustness across different annotation schemes.

Proposed method

• Formulate temporal relation extraction as a structured prediction problem where the output is a globally consistent temporal graph, not independent pairwise labels.
• Use a structured perceptron algorithm to jointly update local models based on feedback from global inference, ensuring symmetry and transitivity constraints are satisfied.
• Integrate unlabeled data via a co-training-like strategy (CoDL) that alternates between labeled and unlabeled data to refine predictions.
• Apply integer linear programming (ILP) to enforce global consistency on predicted relations, correcting inconsistencies from local models.
• Incorporate a post-filtering mechanism to refine predictions by removing inconsistent or vague relations, improving precision.
• Combine predictions from E-T TLINKs (from CAEVO) with E-E TLINKs from the proposed method to enhance overall performance.

Experimental results

Research questions

• RQ1 Can a structured learning approach that enforces global consistency (e.g., transitivity and symmetry) improve temporal relation extraction beyond local models?
• RQ2 How effective is the proposed method in handling missing relations in human-annotated temporal data?
• RQ3 To what extent can unlabeled data be leveraged in a semi-supervised manner to improve temporal relation extraction?
• RQ4 Does the structured approach outperform state-of-the-art systems like CAEVO and ClearTK across different annotation schemes?
• RQ5 Can the method maintain high recall while improving precision through post-filtering and global consistency enforcement?

Key findings

• The proposed CoDL+ILP method achieved an F1 score of 48.53 on the TD-Test dataset, significantly outperforming previous state-of-the-art systems.
• On the TE3-PT dataset, CoDL+ILP achieved a 14.8% relative improvement in F1 score over ClearTK, demonstrating strong performance on sparsely annotated data.
• The SP+ILP variant outperformed CAEVO on TD-Test, with a 6.3% relative improvement in F1 score when using the TE3-SV unlabeled dataset.
• Post-filtering significantly improved performance by reducing inconsistent predictions, showing that structured learning enables better handling of vague or ambiguous relations.
• The method demonstrated robustness across different annotation schemes, with higher performance on the denser TD-Test set compared to the sparser TE3-PT set.
• Despite high recall, precision was lower on TE3-PT, which the authors attribute to missing annotations, indicating that data quality heavily influences performance metrics.