@inproceedings{purushothama-etal-2026-syntax,
title = "Syntax as a Rosetta Stone: {U}niversal {D}ependencies for In-Context {C}optic Translation",
author = "Purushothama, Abhishek and
Thronson, Emma and
Guo, Alexia and
Zeldes, Amir",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1803/",
pages = "36176--36195",
ISBN = "979-8-89176-395-1",
abstract = "This paper proposes a novel in-context learning approach to support low resource machine translation for the Coptic language, using prompts based on Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs, specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and explanations of specific difficult constructions identified in input subgraphs and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art results for the language."
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<abstract>This paper proposes a novel in-context learning approach to support low resource machine translation for the Coptic language, using prompts based on Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs, specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and explanations of specific difficult constructions identified in input subgraphs and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art results for the language.</abstract>
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%0 Conference Proceedings
%T Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation
%A Purushothama, Abhishek
%A Thronson, Emma
%A Guo, Alexia
%A Zeldes, Amir
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F purushothama-etal-2026-syntax
%X This paper proposes a novel in-context learning approach to support low resource machine translation for the Coptic language, using prompts based on Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs, specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and explanations of specific difficult constructions identified in input subgraphs and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art results for the language.
%U https://aclanthology.org/2026.findings-acl.1803/
%P 36176-36195
Markdown (Informal)
[Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation](https://aclanthology.org/2026.findings-acl.1803/) (Purushothama et al., Findings 2026)
ACL