Research·arXiv cs.CL·May 5

Rational Communication Shapes Morphological Composition

Researchers apply rational speech act theory to explain why languages settle on specific morpheme combinations rather than equally plausible alternatives. By modeling morphological composition as a speaker optimization problem balancing listener comprehension against production effort, the work bridges cognitive linguistics and computational modeling in ways relevant to how language models learn and generate word forms. This framework could inform better tokenization strategies and morphological reasoning in NLP systems.

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Explainer

The paper treats morpheme selection as a listener-comprehension versus speaker-effort tradeoff, not just a historical accident or frequency pattern. This reframes morphology as an optimization problem that language models might be implicitly solving during training.

This connects directly to the MemCoE work from early May, which also modeled LLM behavior as learnable optimization under constraints (memory budget, context window). Both papers move away from static rules toward principled tradeoff frameworks. The morphology paper also echoes the modularity-first insight from HyCOP (hybrid composition operators), where constrained, interpretable choices outperform end-to-end learning. If language models are already settling on morpheme combinations that balance comprehension and effort, that suggests tokenization strategies derived from this framework could improve both efficiency and generalization.

If researchers apply this rational speech acts framework to design a tokenizer and show it reduces perplexity on out-of-domain text compared to BPE or SentencePiece, that confirms the theory has practical bite. Otherwise it remains a post-hoc explanation of existing morphology without predictive power for NLP system design.

Coverage we drew on

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory · arXiv cs.CL

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MentionsRational Speech Acts framework · Frank & Goodman · Gibson et al. · arXiv

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