@inproceedings{solla-etal-2026-incorporating,
title = "Incorporating Multiword Expressions in {G}alician Neural Machine Translation: Compositionality, Efficiency, and Performance",
author = "Solla, Daniel and
Pinto-Ferro, Paula and
Castro, Laura and
Gamallo, Pablo and
Garcia, Marcos",
editor = {Ojha, Atul Kr. and
Mititelu, Verginica Barbu and
Constant, Mathieu and
Stoyanova, Ivelina and
Do{\u{g}}ru{\"o}z, A. Seza and
Rademaker, Alexandre},
booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mwe-1.9/",
pages = "75--85",
ISBN = "979-8-89176-363-0",
abstract = "This paper explores the behavior of neural machine translation models on two newly introduced datasets containing noun-adjective MWEs with different degrees of semantic ambiguity and compositionality. We compare general-domain machine translation systems with fine-tuned models exposed to small subsets of the target MWEs. By assessing the effects of the learning steps and corpus size, we found that carefully designed fine-tuned may improve MWE handling while mitigating catastrophic forgetting. However, our error analysis reveals that models still struggle in several scenarios, particularly when translating MWEs with idiomatic meanings. Both the datasets and the experiments focus on translation involving Galician, English, and Spanish."
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%0 Conference Proceedings
%T Incorporating Multiword Expressions in Galician Neural Machine Translation: Compositionality, Efficiency, and Performance
%A Solla, Daniel
%A Pinto-Ferro, Paula
%A Castro, Laura
%A Gamallo, Pablo
%A Garcia, Marcos
%Y Ojha, Atul Kr.
%Y Mititelu, Verginica Barbu
%Y Constant, Mathieu
%Y Stoyanova, Ivelina
%Y Doğruöz, A. Seza
%Y Rademaker, Alexandre
%S Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-363-0
%F solla-etal-2026-incorporating
%X This paper explores the behavior of neural machine translation models on two newly introduced datasets containing noun-adjective MWEs with different degrees of semantic ambiguity and compositionality. We compare general-domain machine translation systems with fine-tuned models exposed to small subsets of the target MWEs. By assessing the effects of the learning steps and corpus size, we found that carefully designed fine-tuned may improve MWE handling while mitigating catastrophic forgetting. However, our error analysis reveals that models still struggle in several scenarios, particularly when translating MWEs with idiomatic meanings. Both the datasets and the experiments focus on translation involving Galician, English, and Spanish.
%U https://aclanthology.org/2026.mwe-1.9/
%P 75-85
Markdown (Informal)
[Incorporating Multiword Expressions in Galician Neural Machine Translation: Compositionality, Efficiency, and Performance](https://aclanthology.org/2026.mwe-1.9/) (Solla et al., MWE 2026)
ACL