---
title: "Who cites Language Modeling for Morphologically Rich…"
description: "33 papers of the Elamur Research library cite «Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction»…"
canonical: "https://research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/cited-by/"
---
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# Who cites «Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction»

33 papers of our library cite it (of the 140,884 whose bibliographies we read) · 4 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/)

1. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/)
2. [**Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings**Ahmed El-Kishky, Xingyu Fu, Aseel Addawood, et al.*2019 · 6 citations*](https://md.research.elamur.ai/free/en/papers/kishky-2019-constrained-sequence-sequence-semitic-root/)
3. [**MoVoC: Morphology-Aware Subword Construction for Ge'ez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction/)
4. [**MoVoC: Morphology-Aware Subword Construction for Geez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction-geez/)

29 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/) — Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction