Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns (2310.01749v2)

Abstract: Attention, specifically scaled dot-product attention, has proven effective for natural language, but it does not have a mechanism for handling hierarchical patterns of arbitrary nesting depth, which limits its ability to recognize certain syntactic structures. To address this shortcoming, we propose stack attention: an attention operator that incorporates stacks, inspired by their theoretical connections to context-free languages (CFLs). We show that stack attention is analogous to standard attention, but with a latent model of syntax that requires no syntactic supervision. We propose two variants: one related to deterministic pushdown automata (PDAs) and one based on nondeterministic PDAs, which allows transformers to recognize arbitrary CFLs. We show that transformers with stack attention are very effective at learning CFLs that standard transformers struggle on, achieving strong results on a CFL with theoretically maximal parsing difficulty. We also show that stack attention is more effective at natural LLMing under a constrained parameter budget, and we include results on machine translation.

Analysis of "Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns"

The paper in question introduces a novel attention mechanism, stack attention, to address the limitations of standard transformers in handling hierarchical syntactic structures inherent in natural language. The authors, DuSell and Chiang, propose two variants of stack attention inspired by the theoretical connections between stack data structures and context-free languages (CFLs). These two variants are deterministic pushdown automata (PDAs) and nondeterministic PDAs. By integrating these into transformers, the authors aim to enhance the modeling capacity of transformers for hierarchical patterns without requiring syntactic supervision.

Proposed Methodology

Stack attention is designed to mimic the operations of stack data structures, thus capturing hierarchical patterns more naturally. This approach builds upon the notion that pushdown automata (PDAs) can recognize CFLs — a class of formal languages that embody recursive and compositional natural language structures. The researchers adapt differentiable stacks from prior work and enrich transformers with stack attention sublayers. Within this framework, stack actions operate over sequences of input vectors, accommodating pushes and pops over a latent stack-like structure.

The paper delineates two core types of stack attention:

1. Superposition Stack Attention: This model performs a weighted superposition of actions, with actions determining whether to push, pop, or perform no operation on the stack continuously. It aligns closely with real-time deterministic PDAs.
2. Nondeterministic Stack Attention: More complexly, this variant allows for nondeterministic behavior akin to nondeterministic PDAs, thereby modeling a broader class of CFLs.

Empirical Evaluations

To substantiate their claims, the authors conducted experiments on several CFL LLMing tasks, a natural LLMing benchmark (Penn Treebank), and a machine translation task (a subset of Europarl v7). The experimental results illustrate profound insights:

1. CFL Tasks: The non-deterministic stack attention variant showed superior performance across tasks involving nondeterministic CFLs, such as balanced strings and hardest CFLs. Notably, it significantly outpaced traditional transformers and other stack-attached LSTM models, especially in modeling the hardest CFL.
2. Natural LLMing: Though the superposition stack didn't surpass standard transformers in perplexity metrics on the Penn Treebank dataset, nondeterministic stack attention achieved better results, suggesting improved data efficiency under constrained parameter limits.
3. Machine Translation: Stack attention did not consistently outperform standard transformers; however, it showed promise in specific parameter integrations, indicating its potential in low-resource scenarios or with alternative configurations.

Implications and Future Trajectories

The integration of stack attention with transformers provides a compelling direction to tackle the syntactic inadequacies of traditional attention mechanisms. By allowing transformers to encapsulate a broader range of syntactic structures similarly to how PDAs process CFLs, stack attention could enhance language understanding models, especially in domains requiring nested patterns and hierarchical reasoning.

The theoretical grounding in CFLs presents a rich avenue for future research — extending stack attention to other models or integrating it with existing hierarchical language understanding frameworks. The lack of necessity for syntactic supervision is an attractive feature, especially in scaling AI models where labeled data may be scarce. However, the computational complexity, particularly that of nondeterministic stack attention, could pose challenges in large-scale deployments, requiring further optimization and or novel architectures.

In conclusion, this paper contributes a significant methodological innovation to the field of NLP and machine learning, and while practical challenges remain, the foundational insights and empirical validations presented offer a robust basis for continued exploration and refinement of hierarchical pattern modeling in AI.