Word meaning in minds and machines (2008.01766v3)

Abstract: Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in NLP. Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss more promising approaches to grounding NLP systems and argue that they will be more successful with a more human-like, conceptual basis for word meaning.

• The paper's main contribution shows that current NLP systems capture word similarity but struggle with the full complexity of human semantic cognition.
• It critiques classical and neural models, emphasizing their reliance on text patterns and their failure to incorporate perceptual and conceptual information.
• The findings advocate for multimodal, neuro-symbolic approaches that integrate language with sensory experience to better mirror human understanding.

Analysis of "Word Meaning in Minds and Machines"

The paper "Word meaning in minds and machines" by Brenden M. Lake and Gregory L. Murphy critically explores the capabilities of contemporary NLP systems in modeling the semantics of human language. It presents the limitations inherent in current models, which derive meaning predominately from text-based patterns and lack grounding in perception, action, and a conceptual understanding akin to human cognition.

Key Thesis and Argumentation

The authors argue that although modern NLP systems exhibit proficiency in replicating human judgments of word similarity, they fall short in encompassing the complex, multifaceted ways in which humans understand and communicate meaning. They advocate for a paradigm shift towards models that intricately link linguistic elements with conceptual structures and sensory experiences, asserting that these would more accurately reflect the cognitive processes underlying word meaning in the human mind.

Methodological Insights and Detailing

Lake and Murphy embark on a methodological discussion of how semantics is traditionally understood in cognitive science versus NLP. They dissect the nature of semantic representation, pointing out gaps between human cognitive models and current machine-learning approaches. They propose several desiderata for a complete psychological semantics model, emphasizing the necessity for models to integrate the comprehension of perceptual input, goals, actions, and flexible conceptual combinations.

Critique of Current Systems

A detailed critique is conducted on existing computational approaches, evaluating both classical models like Latent Semantic Analysis (LSA) and cutting-edge systems involving deep learning and large-scale transformers such as BERT and GPT-2. The authors highlight the strengths in pattern recognition these models exhibit, yet delineate fundamental shortcomings faced by machine systems in understanding emergent meanings, responding appropriately to novel inquiries, and capturing dynamic interactions between language and world-based contexts.

Numerical and Theoretical Implications

The discussants provide sharp insights into experimental findings related to word similarity and composition, where newer machine learning models outperform older paradigms yet continue to struggle with generalization beyond specific training data. They argue for a richer, psychologically grounded basis for semantics that advances the capability of NLP systems to comprehend the nuanced and abstract characteristics inherent in human communication.

Future Directions

The potential for future advancements is deliberated upon, envisioning the role of multimodal models in bridging textual representations and perceptual realities to forge connections between language and action. Emerging trends in neuro-symbolic modeling and hybrid approaches are noted as potential pathways to achieving a deeper, functional semantic understanding in AI systems.

Conclusion

The paper concludes by emphasizing the importance of structured, relational knowledge in LLMs, analogous to human concepts, to reconcile the semantic task from an engineering challenge to a genuinely cognitive one. It posits that successful semantic models will require the integration of broader cognitive and perceptual frameworks, moving beyond word proximities to articulate meanings that are richly embedded in human-like experiences and understanding. This proposition instigates a future-facing discourse on the development trajectory of artificial intelligence, extending the capabilities of machine systems to approximate the complexities of human psychological semantics.