Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

Abstract: Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost---The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution---We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

• The paper introduces a deep reinforcement learning model that dynamically filters false positive training samples in distant supervision relation extraction.
• The method reallocates misclassified sentences as negative examples, significantly enhancing precision and recall on benchmark datasets.
• This DRL approach is model-independent, enabling seamless integration with existing extraction frameworks to mitigate noisy annotations in NLP tasks.

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

The paper "Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning" presents a novel approach to improving the accuracy of relation extraction from text data using distant supervision. This task involves identifying and categorizing relationships between entities in sentences, an essential component in constructing knowledge graphs and enabling sophisticated natural language processing applications.

Traditional methods of relation extraction via distant supervision suffer from the challenge of noisy training data. Noisy data arise from the automatic generation of training samples, where not all samples accurately represent the intended relationships. Many state-of-the-art models attempt to mitigate this issue through soft attention mechanisms, which weigh the contribution of different sentences, or by choosing a "one-best" sentence approach. However, these methods remain suboptimal due to their incapacity to fully address the false positive problem—incorrectly labeled examples mistakenly classified as positive examples.

This research innovatively proposes using deep reinforcement learning (DRL) to tackle these false positives in distant supervision. The authors design a DRL framework to dynamically identify and manage false positives across entity-relation pairs. The proposed method distinguishes itself by reallocating incorrectly labeled sentences into a negative example set, as opposed to the typical strategy of simple removal. This redistribution, accomplished by an RL-based policy, aims to improve the reliability of training data and, consequently, the performance of relation classification.

Key contributions include:

• Introduction of a novel deep reinforcement learning model for filtering false positive samples in distant supervision relation extraction.
• General applicability of the model, as it is designed to be model-independent and can seamlessly integrate with existing relation extraction frameworks.
• Demonstrated performance gains on the New York Times dataset, a prominent benchmark in the field, highlighting the efficacy of the method against leading neural network models.

Empirical results substantiate the claims, showing that application of the RL framework yields performance improvements over baseline models that do not employ such strategies. The method's success is measured through notable improvements in the $F_1$ scores, showcasing enhanced precision and recall in identifying true relational instances between entities.

The application of DRL in this context has broad implications. It not only enhances current methodologies in handling noisy supervision for relational learning but also sets a precedent for adopting reinforcement learning in various NLP tasks struggling with uncertain annotations. Future work may explore optimizing the reward function used in the RL framework or adapting the proposed methodology to other language domains and tasks that require robustness against data noise.

This paper offers compelling evidence of the potential of DRL to advance relation extraction and, by extension, improve the automated construction of knowledge bases, crucial for semantic understanding and complex NLP systems.