Neural Snowball for Few-Shot Relation Learning (1908.11007v2)

Abstract: Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.

An Overview of "Neural Snowball for Few-Shot Relation Learning"

The paper "Neural Snowball for Few-Shot Relation Learning" by Tianyu Gao et al. introduces a method to address the challenge of extracting new relations in knowledge graphs (KGs) with limited instances. This work focuses on overcoming the traditional limitations where relation extraction (RE) models are heavily reliant on large datasets with predefined relations, which is not feasible for handling the dynamic and open-ended growth of real-world KGs.

Core Contributions and Methodology

The primary contribution of this research is the design of the Neural Snowball methodology, a novel bootstrapping approach that utilizes transfer learning for few-shot relation learning. This approach leverages the semantic knowledge from existing relations through a neural framework consisting of two main components:

1. Relational Siamese Networks (RSN): RSN is employed to measure the similarity of instances and efficiently select high-quality instances for new relations. By utilizing a metric-based approach, RSN compares candidate instances against a set of labeled examples, aiding in the iterative identification and aggregation of relevant instances. The network uses dual encoders to encode instance pairs and computes a similarity score, which is pivotal in determining the confidence of candidate instance relevance.
2. Iterative Snowball Process: The core iterative process of Neural Snowball involves starting with a small seed set of instances for a new relation and systematically accumulating further instances from unlabeled corpora. This is conducted in two phases: initially, instances with matching entity pairs are included, then, instances with varying entity pairs that show high confidence according to the relation classifier are added. This cyclical growth mimics the accumulation of a snowball, thereby enhancing the classifier's training dataset.

Numerical Results and Implications

The authors provide comprehensive experimental results demonstrating the effectiveness of Neural Snowball in the few-shot learning scenario. They compare their model against multiple baselines, including conventional bootstrapping methods like BREDS and advanced techniques like fine-tuning pre-trained neural models. The results indicate significant improvements in both precision and recall compared to other models, particularly when utilizing BERT as the encoder method. For instance, Neural Snowball with BERT achieved F1 scores of 47.26%, 61.34%, and 72.06% with 5, 10, and 15 seed instances, respectively, showcasing its robustness in extracting relations with sparse data.

Theoretical and Practical Implications

The Neural Snowball approach brings important theoretical advancements in few-shot learning for relation extraction, highlighting the utility of transfer learning and bootstrapping in dynamic information environments like KGs. Practically, this method allows for the efficient updating and scaling of KGs with minimal manual annotations, providing a scalable solution for real-time information systems.

Future Developments

The paper identifies potential areas for further improvement, such as the exploration of more diverse pattern discovery beyond the established "comfort zone" and the development of adaptive RSN that can be fine-tuned for specific new relations. These advancements could enhance the ability of relation extraction models to adapt to rapidly evolving data conditions in KGs.

Overall, "Neural Snowball for Few-Shot Relation Learning" presents a compelling framework for improving the adaptability and scalability of relation extraction systems within the domain of knowledge graphs, offering valuable insights and techniques for the development of intelligent information systems.