Linguistic generalization and compositionality in modern artificial neural networks

Review of the paper “Linguistic generalization and compositionality in modern artificial neural networks”

Linguistic generalization and compositionality in modern artificial neural networks

In (Baroni 2020) the author discusses the role of deep artificial neural networks in natural language processing tasks and their generalization abilities. The paper reviews the innovations characterizing modern deep language processing networks, such as large training datasets, gated recurrent networks, encoder-decoder architectures, and attention mechanisms. It then delves into studies examining the generalization abilities of deep networks, particularly in the context of grammatical generalization and compositional tasks. The paper presents empirical evidence suggesting that deep networks are capable of subtle grammar-dependent generalizations but do not rely on systematic compositional rules. It discusses the implications of these findings for linguistics and cognitive science, highlighting the need for better analytical tools to understand the mechanisms underlying the intriguing behavior of deep networks in language processing tasks.

Abstract

In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: Are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behavior of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it offers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language.

— (Baroni 2020)

Baroni, Marco. 2020. “Linguistic Generalization and Compositionality in Modern Artificial Neural Networks.” Philosophical Transactions of the Royal Society B 375 (1791): 20190307.

1. Introduction

• Mentions the history of neural networks as tools for modeling cognitive phenomena and their recent success as machine learning algorithms, particularly in natural language processing.
• Highlights the need to understand how neural networks achieve their language skills, given their impressive performance in tasks such as machine translation.
• Presents the argument for studying deep networks from a comparative psychology perspective, to gain insights into the nature of linguistic tasks and how computational devices solve them.
• Discusses the concept of linguistic productivity, its relation to compositionality, and the debate on whether neural networks possess this ability, focusing on the intriguing generalization patterns observed in deep networks.

2. Modern Deep Networks for Language Processing: What Has Changed

• Discusses the role of large training datasets in enhancing neural network performance, enabling complex, multi-layer architecture training, and facilitating language modeling as a general-purpose approach.
• Describes gated recurrent networks, such as LSTMs and GRUs, with their information flow regulation mechanisms (gates) enhancing control and performance.
• Explains the innovation of encoder-decoder architectures, decoupling input and output processing for effective handling of sequence-to-sequence tasks, commonly used in machine translation.
• Presents the concept of attention mechanisms, enabling dynamic information access from past states, further improving the performance of encoder-decoder architectures, particularly in models relying heavily on attention over recurrent connections.

3. Colorless Green Grammatical Generalization in Deep Networks

• Discusses the generalization abilities of modern language-processing neural networks, particularly in the context of machine translation, and the ongoing research into understanding the basis of this performance (shallow heuristics vs. grammar-based generalizations).
• Presents Gulordava et al.’s study testing grammatical generalization in recurrent networks using controlled, nonsensical sentences designed to assess long-distance number agreement, showing above-chance performance across multiple languages.
• Highlights Lakretz et al.’s ablation and connectivity studies of the Gulordava network, revealing specialized units sensitive to long-distance number information and connected to sub-networks sensitive to syntactic constituency, suggesting genuine grammatical processing mechanisms.
• Notes that while networks demonstrate grammatical productivity in these studies, further research is needed to determine if they possess a rule-based system akin to traditional linguistic theory.

4. Compositional Generalization: Can Deep Networks Dax Twice?

• Introduces the SCAN benchmark, a miniature language designed to test compositional abilities in sequence-processing networks, where networks learn to map commands to action sequences.
• Explains the random split in SCAN, testing generic productivity, and the jump split, testing systematic compositionality by introducing a novel verb (“jump”) only in isolation during training and evaluating its use in composite commands during testing.
• Describes the around-right split, controlling for distributional biases in the jump split, where networks are trained on commands excluding the combination of “around” and “right” but with sufficient evidence of their individual behavior.
• Presents results showing recurrent networks excel in the random split but fail in the jump and around-right splits, while a convolutional network with heavy attention achieves partial success, suggesting a non-systematic form of generalization.

5. Conclusion

• Summarizes the surprising empirical evidence suggesting modern deep networks are proficient in language processing without demonstrably possessing compositional rules.
• Discusses the need for better analytical tools to understand the mechanisms underlying this dissociation between productive grammatical competence and systematic compositionality.
• Highlights the importance of exploring whether enhancing compositionality in neural networks can lead to improved adaptability and learning speed, potentially addressing their current limitations in tasks like machine reading and natural language inference.
• Notes the potential for comparative studies between neural networks and human language, offering insights into the less systematic and fuzzy aspects of linguistic productivity, ultimately contributing to a more comprehensive understanding of human language.

The paper

Linguistic generalization and compositionality in modern artificial neural networks