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Better Character Language Modeling Through Morphology

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arxiv 1906.01037 v2 pith:PM5UI6ZO submitted 2019-06-03 cs.CL

Terra Blevins , Luke Zettlemoyer This is my paper
classification cs.CL
keywords languagemodelingdatamorphologicalmorphologyperformancesupervisionacross
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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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Cited by 1 Pith paper

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  1. Discrete Diffusion Models for Language Generation

    cs.CL 2025-07 reject novelty 3.0 of 10

    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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