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Elizabeth Salesky

8 accepted papers

2025

WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects

Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska +11
ACL 2025finding

As large language models (LLM) become more and more capable in languages other than English, it is important to collect benchmark datasets in order to evaluate their multilingual performance, including on tasks like machine translation (MT). In this work, we extend the WMT24 dataset to cover 55 lang…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation

Matthias Sperber, Ondřej Bojar, Barry Haddow, Dávid Javorský, Xutai Ma, Matteo Negri +5
COLING 2024main

Human evaluation is a critical component in machine translation system development and has received much attention in text translation research. However, little prior work exists on the topic of human evaluation for speech translation, which adds additional challenges such as noisy data and segmenta…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

A Holistic Cascade System, Benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation

Wen-Chin Huang, Benjamin N. Peloquin, Justine Kao, Changhan Wang, Hongyu Gong, Elizabeth Salesky +3
ICASSP 2023accepted

Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in expressive S2ST is limited, typically focusing on a single expressivity aspect at a time. Likewise, this research area la…

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2023

Language Modelling with Pixels

Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
ICLR 2023top-5%

Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck results in a trade-off between what can be represented in the embedding matrix and computational issues in the output la…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2023

Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer

Elizabeth Salesky, Neha Verma, Philipp Koehn, Matt Post
EMNLP 2023long main

We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations. We experiment with two different data settings with a variety of language and script coverage, demonstrating improved performance compared to subword embeddings. We explore vario…

Cited by 0Source ↗Scholar ↗
2023

Text Rendering Strategies for Pixel Language Models

Jonas F. Lotz, Elizabeth Salesky, Phillip Rust, Desmond Elliott
EMNLP 2023long main

Pixel-based language models process text rendered as images, which allows them to handle any script, making them a promising approach to open vocabulary language modelling. However, recent approaches use text renderers that produce a large set of almost-equivalent input patches, which may prove sub-…

Cited by 23Source ↗Scholar ↗
2021

A surprisal–duration trade-off across and within the world’s languages

Tiago Pimentel, Clara Meister, Elizabeth Salesky, Simone Teufel, Damián Blasi, Ryan Cotterell
EMNLP 2021main

While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process in…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2021

Robust Open-Vocabulary Translation from Visual Text Representations

Elizabeth Salesky, David Etter, Matt Post
EMNLP 2021main

Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary.’ This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. M…

Cited by 43Source ↗PDF ↗code ↗Scholar ↗