@inproceedings{oncevay-etal-2026-simple,
title = "Simple Beats Spend: Cost-Performance Trade-Offs for Multi-Agent Machine Translation of Financial Long Documents",
author = "Oncevay, Arturo and
Guo, Zhijin and
Kochkina, Elena and
Fassina, Damiano and
Kaur, Simerjot and
Smiley, Charese and
Liu, Xiaomo and
Veloso, Manuela",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1767/",
pages = "37804--37818",
ISBN = "979-8-89176-462-0",
abstract = "Agentic MT frameworks often adopt human-inspired roles, layered post-editing, and complex workflows. To stress-test these designs, we examine cost{--}performance trade-offs across five multi-agent MT workflows on specialized long documents. Using six language pairs from two financial test sets and LLMs spanning lower to higher capacity and cost tiers, we evaluate translation quality and terminology accuracy relative to token-level cost. We find: (i) lean pipelines match the quality of more complex systems at lower cost; (ii) terminology accuracy depends more on model capacity than on workflow complexity; (iii) document-length resilience is not universal, as some simple and some complex workflows maintain quality on longer documents; and (iv) post-editing agents offer a better return on model capacity than base translation. Edited-span analysis further suggests that many post-edits are stylistic or lexical alternatives rather than consistently preferred improvements."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="oncevay-etal-2026-simple">
<titleInfo>
<title>Simple Beats Spend: Cost-Performance Trade-Offs for Multi-Agent Machine Translation of Financial Long Documents</title>
</titleInfo>
<name type="personal">
<namePart type="given">Arturo</namePart>
<namePart type="family">Oncevay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhijin</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Kochkina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Damiano</namePart>
<namePart type="family">Fassina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simerjot</namePart>
<namePart type="family">Kaur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Charese</namePart>
<namePart type="family">Smiley</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaomo</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manuela</namePart>
<namePart type="family">Veloso</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Agentic MT frameworks often adopt human-inspired roles, layered post-editing, and complex workflows. To stress-test these designs, we examine cost–performance trade-offs across five multi-agent MT workflows on specialized long documents. Using six language pairs from two financial test sets and LLMs spanning lower to higher capacity and cost tiers, we evaluate translation quality and terminology accuracy relative to token-level cost. We find: (i) lean pipelines match the quality of more complex systems at lower cost; (ii) terminology accuracy depends more on model capacity than on workflow complexity; (iii) document-length resilience is not universal, as some simple and some complex workflows maintain quality on longer documents; and (iv) post-editing agents offer a better return on model capacity than base translation. Edited-span analysis further suggests that many post-edits are stylistic or lexical alternatives rather than consistently preferred improvements.</abstract>
<identifier type="citekey">oncevay-etal-2026-simple</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.1767/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>37804</start>
<end>37818</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Simple Beats Spend: Cost-Performance Trade-Offs for Multi-Agent Machine Translation of Financial Long Documents
%A Oncevay, Arturo
%A Guo, Zhijin
%A Kochkina, Elena
%A Fassina, Damiano
%A Kaur, Simerjot
%A Smiley, Charese
%A Liu, Xiaomo
%A Veloso, Manuela
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F oncevay-etal-2026-simple
%X Agentic MT frameworks often adopt human-inspired roles, layered post-editing, and complex workflows. To stress-test these designs, we examine cost–performance trade-offs across five multi-agent MT workflows on specialized long documents. Using six language pairs from two financial test sets and LLMs spanning lower to higher capacity and cost tiers, we evaluate translation quality and terminology accuracy relative to token-level cost. We find: (i) lean pipelines match the quality of more complex systems at lower cost; (ii) terminology accuracy depends more on model capacity than on workflow complexity; (iii) document-length resilience is not universal, as some simple and some complex workflows maintain quality on longer documents; and (iv) post-editing agents offer a better return on model capacity than base translation. Edited-span analysis further suggests that many post-edits are stylistic or lexical alternatives rather than consistently preferred improvements.
%U https://aclanthology.org/2026.findings-emnlp.1767/
%P 37804-37818
Markdown (Informal)
[Simple Beats Spend: Cost-Performance Trade-Offs for Multi-Agent Machine Translation of Financial Long Documents](https://aclanthology.org/2026.findings-emnlp.1767/) (Oncevay et al., Findings 2026)
ACL
- Arturo Oncevay, Zhijin Guo, Elena Kochkina, Damiano Fassina, Simerjot Kaur, Charese Smiley, Xiaomo Liu, and Manuela Veloso. 2026. Simple Beats Spend: Cost-Performance Trade-Offs for Multi-Agent Machine Translation of Financial Long Documents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 37804–37818, Budapest, Hungary. Association for Computational Linguistics.