Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings

Abstract: Transformers generalize to novel compositions of structures and entities after being trained on a complex dataset, but easily overfit on datasets of insufficient complexity. We observe that when the training set is sufficiently complex, the model encodes sentences that have a common syntactic structure using a systematic attention pattern. Inspired by this observation, we propose SQ-Transformer (Structurally Quantized) that explicitly encourages systematicity in the embeddings and attention layers, even with a training set of low complexity. At the embedding level, we introduce Structure-oriented Vector Quantization (SoVQ) to cluster word embeddings into several classes of structurally equivalent entities. At the attention level, we devise the Systematic Attention Layer (SAL) and an alternative, Systematically Regularized Layer (SRL) that operate on the quantized word embeddings so that sentences of the same structure are encoded with invariant or similar attention patterns. Empirically, we show that SQ-Transformer achieves stronger compositional generalization than the vanilla Transformer on multiple low-complexity semantic parsing and machine translation datasets. In our analysis, we show that SoVQ indeed learns a syntactically clustered embedding space and SAL/SRL induces generalizable attention patterns, which lead to improved systematicity.

• The paper introduces SQ-Transformer by integrating structure-oriented vector quantization with systematic attention layers to enhance compositional generalization.
• Empirical results demonstrate superior performance over standard Transformers, with higher BLEU scores and reduced error rates on SCAN, COGS, and CoGnition benchmarks.
• The research highlights that leveraging structural linguistic insights with low-complexity data opens avenues for efficient, data-sparse models, guiding future exploration of higher-order syntactic quantization.

Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings

This paper addresses a central challenge in neural network-based natural language processing: the capacity for compositional generalization. Standard Transformers show limited generalization to novel compositions, especially with insufficient training data complexity. This work proposes the SQ-Transformer to enhance the systematicity in Transformers using structurally quantized embeddings.

Core Contributions:

The paper introduces several key innovations to tackle the challenge:

1. Structure-oriented Vector Quantization (SoVQ): This is a mechanism to cluster word embeddings into classes of structurally equivalent entities. By leveraging SoVQ, embeddings are encouraged to encode structural patterns rather than purely semantic similarities.
2. Systematic Attention Layer (SAL) and Systematically Regularized Layer (SRL): Two novel attention mechanisms are proposed. SAL operates on quantized word embeddings, ensuring that structurally similar sentences are encoded through invariant attention patterns. SRL, an alternative to SAL, regularizes the attention outputs by enforcing soft invariance, allowing some flexibility in encoding non-structural relationships essential for processing natural language nuances.

Empirical Findings:

The effectiveness of SQ-Transformer is demonstrated through its superior performance over vanilla Transformers across several benchmarks:

• It achieves improved accuracy on SCAN's AddJump and AroundRight tasks, underlying its enhanced compositional generalization.
• On the COGS and CoGnition datasets, SQ-Transformer shows significantly higher BLEU scores and lower novel compound translation error rates compared to baseline models, indicating its efficacy in machine translation tasks.

The results highlight that SQ-Transformer not only excels in purely synthetic tasks but also holds promise in more complex, naturally occurring datasets, outperforming other state-of-the-art approaches in many cases.

Implications and Future Research:

The theoretical and practical implications of SQ-Transformer are substantial:

• Theoretical Implications: By clustering word embeddings based on syntactic functions and deploying quantized attention patterns, SQ-Transformer adheres to linguistic principles effectively. The paper challenges prior assertions that neural networks are inherently flawed in capturing compositionality by illustrating that with appropriate regularization and architectural design, Transformers can indeed exhibit robust systematic behavior.
• Practical Implications: The practicality of inducing systematicity using only low-complexity data opens new avenues for constructing computationally efficient models that generalize well even without extensive pre-training datasets.

For future work, investigating the role of structural patterns in models beyond syntactic functions to encompass phrasal constituents and broader discourse structures could be promising. Extending the quantization techniques to higher-order syntactic units could enhance the models' generalization further in complex tasks.

In conclusion, the introduction of SQ-Transformer marks a significant step towards comprehensively understanding and engineering the compositional capabilities of neural LLMs. It weaves together structural linguistic insights with advanced machine learning techniques, laying the groundwork for developing more systematic and data-efficient AI models.