Compositionality Decomposed: How Do Neural Networks Generalise?
In the given paper by Hupkes et al., the authors address a pivotal question within the domain of artificial intelligence and natural language processing: do neural networks generalize compositionally? This inquiry is significant as it targets the often-overlooked intersection of symbolic theoretical frameworks in linguistics and the empirical landscape dominated by statistical models. The authors introduce a set of task-independent tests derived from linguistic and philosophical theories of language compositionality. These tests aim to elucidate the extent to which popular neural network architectures embody compositional principles naturally observed in human languages.
Key Aspects of the Research
To frame their inquiry, the researchers synthesize broad interpretations of compositionality into five key tests that aim to capture distinct facets of compositional generalization:
- Systematicity: Investigating whether models systematically recombine known elements (atomic parts and rules) to understand new sequences.
- Productivity: Evaluating a model's capability to make predictions for sequences beyond the lengths seen during training, reflecting a potential for infinite creativity or productivity.
- Substitutivity: Analyzing the robustness of model predictions in the face of synonym substitutions, thereby understanding synonymy in contextual embeddings.
- Localism: Testing whether neural models apply local composition operations, i.e., evaluating smaller constituents before larger structures to unearth global vs. local processing.
- Overgeneralisation: Gauging whether models incline towards rule application—evaluating the balance between rule governance and exception memorization.
These tests are instantiated on a synthetically generated dataset, PCFG SET, designed to mimic the structural properties of natural language, ensuring relevance in examining compositionality.
Empirical Analysis and Results
The authors apply their evaluation suite to a cohort of neural architectures: LSTMS2S (LSTM-based), ConvS2S (convolution-based), and the Transformer (attention-based). They report several salient findings:
- Task Performance Overall: The Transformer outperforms both the LSTMS2S and the ConvS2S architectures in overall sequence accuracy, suggesting a superior capacity for function-composition and productive generalization.
- Systematicity & Productivity Tests: All models exhibited limitations in systematic recombination and generalization to unseen longer sequences, implying that neural models might rely on memorization over genuine abstraction in these contexts.
- Substitutivity & Localism: There are significant differences in how models treat synonyms and structural decompositions. Transformer models, often superior in synonym handling, demonstrate a capacity to form robust embeddings, whereas localism results unveil challenges in decomposing sequences systematically.
- Overgeneralisation Patterns: ConvS2S and Transformer demonstrated greater adaptability in moving between exceptions and rule-based generalizations compared to LSTMS2S, indicating variations in preference for memorization versus rule learning across architectures.
Broader Implications and Future Directions
The paper's implications extend both theoretically and practically within AI. The reported gaps in systematicity and productivity emphasize areas where neural networks potentially deviate from human-like compositionality, suggesting avenues for architectural innovation or alternative training regimes. Meanwhile, the tests provide a refined framework for understanding compositional generalization, while their application across architectures highlights distinct model behaviors, moving the research toward clearer benchmarks for compositional learning in AI.
The authors rightly propose that future research could extrapolate these findings to real-world data, thus bridging insights from synthetic frameworks to noisy, semantically-rich natural languages. Such an effort would illuminate whether limitations observed in artificial settings persist in practice and how advances in model architectures might address these enduring challenges in compositionality. An evolving dialogue between formal compositional theories and empirical modeling seems not only beneficial but essential for this line of inquiry.