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Better Character Language Modeling Through Morphology

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abstract

We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve language modeling performance in the low-resource setting.

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cs.CL 1

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2025 1

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REJECT 1

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representative citing papers

Discrete Diffusion Models for Language Generation

cs.CL · 2025-07-02 · reject · novelty 3.0

This thesis reports an empirical D3PM versus autoregressive comparison on WikiText-103, but the claimed speed advantage is unsupported because the speed numbers duplicate NLL values.

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  • Discrete Diffusion Models for Language Generation cs.CL · 2025-07-02 · reject · none · ref 3 · internal anchor

    This thesis reports an empirical D3PM versus autoregressive comparison on WikiText-103, but the claimed speed advantage is unsupported because the speed numbers duplicate NLL values.