Sentence Embedding Alignment for Lifelong Relation Extraction (1903.02588v3)

Abstract: Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new data and relations are emerging incessantly and it is computationally expensive to store all data and re-train the whole model every time new data and relations come in. We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks. We first investigate a modified version of the stochastic gradient methods with a replay memory, which surprisingly outperforms recent state-of-the-art lifelong learning methods. We further propose to improve this approach to alleviate the forgetting problem by anchoring the sentence embedding space. Specifically, we utilize an explicit alignment model to mitigate the sentence embedding distortion of the learned model when training on new data and new relations. Experiment results on multiple benchmarks show that our proposed method significantly outperforms the state-of-the-art lifelong learning approaches.

Sentence Embedding Alignment for Lifelong Relation Extraction

The paper "Sentence Embedding Alignment for Lifelong Relation Extraction" investigates a critical challenge in the field of relation extraction, focusing on the limitations of conventional approaches that require a fixed set of predefined relations. This requirement is impractical in dynamic applications where new data and relations emerge continuously, making it computationally prohibitive to store all incoming data and retrain models entirely each time. To address these constraints, the authors formulate a lifelong relation extraction problem grounded in memory-efficient incremental learning, aiming to prevent catastrophic forgetting.

Methodological Overview

The research explores the capabilities of stochastic gradient methods enhanced with replay memory mechanisms. Surprisingly, a modified version of these methods surpasses recent lifelong learning techniques. The authors introduce an explicit alignment model to mitigate the distortion of sentence embeddings when training on new data. This approach effectively anchors the sentence embedding space, offering a promising solution to reduce forgetting.

The methodology is characterized by two main contributions:

1. Replay Memory Approach: This straightforward technique outperforms popular methods like Elastic Weight Consolidation (EWC) and Gradient Episodic Memory (GEM). The simplicity of replay memory leverages stored samples in conjunction with new data through incremental learning.
2. Embedding Alignment Model: By treating stored samples from previous tasks as anchor points, the alignment model minimizes distortions in the embedding space, maintaining model efficacy across tasks.

Experimental Results

Experiments conducted on multiple benchmarks, such as SimpleQuestions and FewRel, demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches. This is evident in metrics such as average accuracy across tasks and accuracy on test data. The alignment model proved crucial in preserving sentence embeddings and reducing performance degradation over sequential tasks.

Implications and Future Directions

The implications of this research are manifold, both practically and theoretically:

• Practical Application: The introduction of an alignment model that maintains the consistency of features across lifelong learning tasks has profound implications for real-world relation extraction systems that face continuously evolving datasets and relations.
• Theoretical Contribution: By shifting the focus from sole reliance on model parameters to embedding spaces, the research provides a fresh perspective on overcoming catastrophic forgetting, highlighting the importance of maintaining stable feature representations over time.

Looking ahead, this research opens avenues for refining alignment models and exploring diverse sample selection methods to further enhance memory efficiency. It also invites exploration into alternative task representations that can further reduce distortion, potentially applicable to broader AI tasks beyond relation extraction.

While the proposed methods offer substantial improvements, the paper suggests considerable potential for refining representative sample selection within memory replay techniques, advancing the integration and functioning of lifelong learning models in dynamic environments.