Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (1810.09536v6)

Abstract: Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: LLMing, unsupervised parsing, targeted syntactic evaluation, and logical inference.

• The paper introduces ON-LSTM, a novel RNN architecture that integrates tree structures via ordered neurons and a cumulative softmax activation to mirror linguistic hierarchies.
• The model demonstrates superior performance in language modeling, unsupervised constituency parsing, and logical inference, effectively handling long-term dependencies.
• The architecture’s hierarchical inductive bias opens new avenues for embedding structural knowledge in neural networks, encouraging future research in syntactic and compositional learning.

Integrating Tree Structures into Recurrent Neural Networks: Ordered Neurons LSTM

The paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks" introduces a novel recurrent neural network (RNN) architecture, the Ordered Neurons LSTM (ON-LSTM), designed to incorporate the hierarchical nature of natural language into the model structure. This work builds upon the well-established Long Short-Term Memory (LSTM) networks by embedding an inductive bias that aligns with the tree-like syntactic structures inherent in human languages.

Model Architecture and Inductive Bias

ON-LSTM leverages an ordered neuron strategy where neurons within the network are arranged to capture hierarchical information reflecting the structure of language constituents. By introducing a vector of master input and forget gates, the architecture ensures that when a given neuron is updated, all neurons following it in the order are similarly updated. This design emulates the nested structures in natural language, enabling the model to maintain and update long and short-term dependencies efficiently.

A key component of ON-LSTM is the cumulative softmax ($cumax(\cdot)$) activation function, which promotes a differentiation in how information is stored within neurons. Neurons high in the ordering store long-term information, while low-ranking neurons capture short-term dependencies. This differentiating capability allows ON-LSTM to perform effectively on tasks requiring understanding of composition hierarchies.

Empirical Performance and Tasks

The ON-LSTM architecture demonstrates strong performance across various tasks:

1. LLMing: The model achieves competitive results on the Penn TreeBank dataset, demonstrating its efficacy in capturing language nuances while maintaining feasible parameter efficiency.
2. Unsupervised Constituency Parsing: By evaluating the model's ability to infer latent tree structures that align with human-annotated parse trees, the ON-LSTM shows superior performance, especially on longer sentences, indicating its robustness and generalization capabilities.
3. Syntactic Evaluation: On targeted syntactic evaluation, ON-LSTM outperforms standard LSTM models in capturing long-term dependencies, though it performs comparably on short-term dependencies.
4. Logical Inference: The model outperforms standard LSTMs on logical inference tasks involving nested logical structures, showcasing its ability to generalize well beyond training configurations, particularly with longer sequences.

Methodological Implications

The introduction of order in the neural architecture enables the ON-LSTM to inherently model the nested structures prominent in linguistic constructs. This addresses several limitations observed in traditional RNNs regarding capturing long-term dependencies and compositional syntax. The design suggests a direction for future research into models with embedded structural biases, which can be crucial for tasks requiring deep syntactic understanding or logic-based reasoning.

Future Directions

Exploring applications of such structurally-biased RNN architectures across a broader spectrum of NLP tasks could lead to further refinements and deeper insights. Potential areas include cross-linguistic syntactic analysis, multi-language learning systems, or even extending the model to more complex hierarchical data found in non-linguistic domains. Additionally, investigating the integration of such biases in transformer-based models could yield advancements given the popularity and effectiveness of transformers in recent years.

This paper advances the understanding of integrating hierarchical biases in neural architectures, showing promising results across a range of natural language understanding tasks. As research in this area progresses, it opens pathways for models that can more naturally align their processing capabilities with the intricacies of human language structure.