Leveraging Knowledge Bases in LSTMs for Improving Machine Reading (1902.09091v1)

Abstract: This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.

Leveraging Knowledge Bases in LSTMs for Improving Machine Reading

This paper presents a noteworthy advancement in the integration of external knowledge bases (KBs) with recurrent neural networks (RNNs) to enhance machine reading capabilities specifically for the tasks of entity extraction and event extraction. The authors, Bishan Yang and Tom Mitchell, propose an innovative framework named Knowledge-augmented Bidirectional Long Short-Term Memory (KBLSTM) that effectively utilizes continuous knowledge representations to surpass the traditional reliance on discrete indicator features derived from KBs, addressing the limitations associated with task-specific feature engineering and poor generalization.

Core Contributions

1. KBLSTM Architecture: Central to this paper is the KBLSTM model, an extension of bidirectional LSTM networks that seamlessly incorporates knowledge from KBs as it processes textual input. The attention mechanism with an added sentinel component enables the model to selectively attend to relevant background knowledge while considering the context from the text. This adaptation mechanism helps mitigate the pitfalls of knowledge features being overly sparse or misleading due to polysemy.
2. Integration of Diverse Knowledge Bases: The paper focuses on leveraging two major KBs—WordNet and NELL—and employs knowledge graph embedding techniques to create continuous vector representations of concepts from these KBs. By doing so, it allows the KBLSTM model to dynamically retrieve and incorporate pertinent knowledge when interpreting text.
3. Experimental Validation: The authors conduct extensive experiments on the ACE2005 dataset for both entity and event extraction tasks. The results indicate a significant improvement over previous state-of-the-art methods, demonstrating the effectiveness of the KBLSTM approach in accurately detecting entity mentions and event triggers through its capability to discern and apply relevant knowledge.

Implications and Future Directions

The proposed KBLSTM model illustrates substantial promise for tasks beyond those tested in the paper, suggesting applications in broader NLP challenges where understanding the nuanced meaning of text is pivotal. By enhancing the ability of LSTMs to contextually utilize external knowledge, this research lays foundational work for future exploration in machine reading comprehension systems that can integrate multiple KBs. Such integration would potentially enhance the robustness of AI models in handling diverse linguistic phenomena encountered in different domains and contexts.

From a theoretical perspective, this work underscores the utility of knowledge-aware neural networks, which may open pathways for more complex models where learned knowledge representations interact seamlessly with learned linguistic representations. Practically, the successful application of these models in real-world scenarios could significantly optimize information extraction processes in sectors such as digital assistants, automated content curation, and intelligent data analytics.

In conclusion, the paper delivers a substantial improvement to machine reading by refining the blend of KBs and RNNs, underscoring the potential for enhanced neural architectures that take full advantage of available semantic knowledge. Future research may further explore diversifications of KB sources and continual learning architectures to refine this innovative approach, thereby advancing the precision and depth of machine reading tasks in various applications.