Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting

3Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

Abstract

Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. Despite significant advances, machine translation systems still struggle to translate idiomatic expressions. We provide a simple characterization of idiomatic translation and related issues. This allows us to conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations. To expand multilingual resources, we compile a dataset of ∼ 4k natural sentences containing idiomatic expressions in French, Finnish, and Japanese. To improve translation of natural idioms, we introduce two straightforward yet effective techniques: the strategic upweighting of training loss on potentially idiomatic sentences, and using retrieval-augmented models. This not only improves the accuracy of a strong pretrained MT model on idiomatic sentences by up to 13% in absolute accuracy, but also holds potential benefits for non-idiomatic sentences.

References Powered by Scopus

Billion-Scale Similarity Search with GPUs

1672Citations
N/AReaders
Get full text

Multiword expressions: A pain in the neck for NLP

691Citations
N/AReaders
Get full text

Processing of idioms by L2 learners of english

146Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology

0Citations
N/AReaders
Get full text

Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Liu, E., Chaudhary, A., & Neubig, G. (2023). Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 15095–15111). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.933

Readers over time

‘23‘24‘2502468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

60%

Lecturer / Post doc 1

20%

Researcher 1

20%

Readers' Discipline

Tooltip

Computer Science 6

86%

Medicine and Dentistry 1

14%

Save time finding and organizing research with Mendeley

Sign up for free
0