Structured Attention Networks (1702.00887v3)

Abstract: Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models, within deep networks. We show that these structured attention networks are simple extensions of the basic attention procedure, and that they allow for extending attention beyond the standard soft-selection approach, such as attending to partial segmentations or to subtrees. We experiment with two different classes of structured attention networks: a linear-chain conditional random field and a graph-based parsing model, and describe how these models can be practically implemented as neural network layers. Experiments show that this approach is effective for incorporating structural biases, and structured attention networks outperform baseline attention models on a variety of synthetic and real tasks: tree transduction, neural machine translation, question answering, and natural language inference. We further find that models trained in this way learn interesting unsupervised hidden representations that generalize simple attention.

• The paper introduces a novel integration of graphical models with attention mechanisms, enabling refined structural inference in neural networks.
• It employs linear-chain CRFs and graph-based parsing models to capture sequence-level dependencies and latent syntactic structures.
• Experimental results demonstrate improved performance in tasks like translation, tree transduction, and question answering, highlighting practical benefits.

Structured Attention Networks: A Technical Overview

The paper "Structured Attention Networks" presents an innovative extension of attention mechanisms within deep learning frameworks, specifically integrating graphical models to capture structural dependencies in data. The core contribution lies in introducing structured attention networks, which accommodate richer structural distributions without forsaking end-to-end training.

Key Concepts and Models

The authors address limitations of standard attention networks, which typically operate under soft-selection approaches and may overlook inherent structural relationships among input elements. To overcome this, they propose two main classes of structured models: linear-chain conditional random fields (CRFs) and graph-based parsing models.

1. Linear-Chain CRFs: This model is used to incorporate sequence-level dependencies, allowing for soft-selection over subsequences rather than individual elements. It is exemplified in tasks like neural machine translation, where segmentation attention layers encourage models to focus on contiguous parts of input sequences.
2. Graph-Based Parsing Models: These models employ parsing strategies to infer recursive structures, such as dependency trees, within input data. The syntactic attention layer, for example, models latent structures in text, leveraging graph-based inference to enrich representations without explicit supervision.

Methodology and Implementation

Structured attention networks expand upon traditional attention by embedding graphical models as internal layers within neural networks. This approach requires the development of differentiable inference mechanisms compatible with neural architectures. The authors innovate by integrating the forward-backward algorithm and inside-outside algorithm for CRFs, allowing for efficient computation of measure-based attention distributions.

The differentiation of these algorithms in log-space ensures numerical stability, which is crucial for handling the intricate computations involved in structured attention. Moreover, the backward pass, crucial for gradient computation, is constructed through reverse-mode differentiation, ensuring end-to-end differentiability.

Experimental Results

Through rigorous experimentation on various tasks, including tree transduction, neural machine translation, question answering, and natural language inference, the structured attention models routinely demonstrated superior performance over conventional attention models. Notably:

• Tree Transduction: The structured attention model reliably uncovered underlying syntactic structures and maintained robustness across differing input complexities.
• Neural Machine Translation: The segmentation attention model effectively managed translation from unsegmented Japanese characters, improving BLEU scores and outperforming standard and sigmoid attention mechanisms.
• Question Answering: The binary CRF model displayed substantial improvements in selecting accurate supporting facts, underscoring the potential of structured inference in reasoning tasks.

Theoretical and Practical Implications

The integration of structured attention networks has significant implications for both theoretical advancements and practical applications. Theoretically, it showcases the viability of embedding complex inference processes directly into neural networks, paving the way for more nuanced and interpretable models. Practically, it offers enhanced capacity for tasks requiring comprehension of structural relationships, such as natural language processing and sequence modeling.

Future Directions

The paper hints at further exciting avenues, such as extending the framework to accommodate approximate inference techniques or exploring differentiable optimization algorithms. These developments could enhance the flexibility and applicability of structured models, particularly in domains requiring sophisticated structural understanding.

In conclusion, structured attention networks emerge as a promising addition to neural architectures, broadening the spectrum of tasks addressable by end-to-end trainable models while offering a deeper insight into the structural intricacies of input data.