Papers by Nathaniel Romney Robinson
Programming by Example meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction (2025.acl-long)
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Atharva Naik, Darsh Agrawal, Hong Sng, Clayton Marr, Kexun Zhang, Nathaniel Romney Robinson, Kalvin Chang, Rebecca Byrnes, Aravind Mysore, Carolyn Rose, David R. Mortensen
| Challenge: | Historical linguists have written programs that convert reconstructed words into their attested descendants via ordered string rewrite functions. |
| Approach: | They propose to use a model to generate a "similar distribution" for sound law induction . they propose four kinds of methods with varying amounts of inductive bias to investigate best performance . |
| Outcome: | The proposed model shows that it can be fine tuned with training data and evaluation data. |
PWESuite: Phonetic Word Embeddings and Tasks They Facilitate (2024.lrec-main)
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Vilém Zouhar, Kalvin Chang, Chenxuan Cui, Nate B. Carlson, Nathaniel Romney Robinson, Mrinmaya Sachan, David R. Mortensen
| Challenge: | Existing word embedding methods overlook phonetic information that is crucial for many tasks. |
| Approach: | They propose three methods that use articulatory features to build phonetically informed word embeddings. |
| Outcome: | The proposed methods improve word retrieval and correlation with sound similarity and on rhyme and cognate detection tasks. |
AfriMMT-EA: Multi-domain Machine Translation for Low-Resource East African Languages (2026.findings-eacl)
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Naome A Etori, Kelechi Ezema, Nathaniel Romney Robinson, Davis David, Alfred Malengo Kondoro, Elisha Ondieki Makori, Michael Samwel Mollel, Maria Gini
| Challenge: | Recent advances in open-source large language models have demonstrated strong multilingual capabilities through data-efficient adaptation strategies. |
| Approach: | They propose to use AfriMMT-EA to refine two multilingual versions of Gemma-3 to better understand the region's linguistic and cultural diversity. |
| Outcome: | The proposed datasets comprise 54 local languages across five East African countries. |
AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic (2025.findings-acl)
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| Challenge: | Dialectal Arabic (DA) varieties are under-served by language technologies, particularly large language models (LLMs). |
| Approach: | They propose a framework that comprehensively assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia. |
| Outcome: | The proposed framework assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia. |
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models (2025.acl-long)
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Niyati Bafna, Emily Chang, Nathaniel Romney Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, Hale Sirin
| Challenge: | Recent advances in MT quality and language coverage have shown that language varieties with low baseline performance are more likely to benefit from these approaches. |
| Approach: | They propose a training-time technique for adapting a pretrained model to dialectal data and an inference-time intervention adapting dialectal datasets to the model expertise. |
| Outcome: | The proposed model shows significant performance gains for several dialects from four language families, and modest gains for two other language families. |
Limited-Resource Adapters Are Regularizers, Not Linguists (2025.acl-short)
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Marcell Fekete, Nathaniel Romney Robinson, Ernests Lavrinovics, Djeride Jean-Baptiste, Raj Dabre, Johannes Bjerva, Heather Lent
| Challenge: | Existing studies show that cross-lingual transfer from high-resource languages is promising for low-resourced machine translation. |
| Approach: | They propose to use adapter souping and cross-attention fine-tuning to leverage language transfer for Creoles, an under-served group of low-resource languages. |
| Outcome: | The proposed method improves performance over baselines but not meaningfully with adapters. |