106 citations · 133 across the 18 of their papers we have counts for
24 papers · 1 filter
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects
Daniel Deutsch, Eleftheria Briakou, Isaac Caswell +14
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 p…
Findings of the IWSLT 2024 Evaluation Campaign
Ibrahim Said Ahmad, Antonios Anastasopoulos, Ondřej Bojar +42
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and off…
Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation
Matthias Sperber, Ondřej Bojar, Barry Haddow +8
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 o…
Text Rendering Strategies for Pixel Language Models
Jonas F. Lotz, Elizabeth Salesky, Phillip Rust +1
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…
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer
Elizabeth Salesky, Neha Verma, Philipp Koehn +1
We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations. We experiment with two different data settings with a vari…
A Holistic Cascade System, benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation
Wen-Chin Huang, Benjamin Peloquin, Justine Kao +6
Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in e…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Language Modelling with Pixels
Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello +3
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…
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky +8
What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural langu…
Assessing Evaluation Metrics for Speech-to-Speech Translation
Elizabeth Salesky, Julian Mäder, Severin Klinger
Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate s…
A surprisal--duration trade-off across and within the world's languages
Tiago Pimentel, Clara Meister, Elizabeth Salesky +3
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 reasonab…
SIGTYP 2021 Shared Task: Robust Spoken Language Identification
Elizabeth Salesky, Badr M. Abdullah, Sabrina J. Mielke +6
While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource an…
Robust Open-Vocabulary Translation from Visual Text Representations
Elizabeth Salesky, David Etter, Matt Post
Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an 'open vocabulary.' This approach relies on consistent and corre…
The Multilingual TEDx Corpus for Speech Recognition and Translation
Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman +5
We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a co…
SIGTYP 2020 Shared Task: Prediction of Typological Features
Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke +6
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be use…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from…
A Corpus for Large-Scale Phonetic Typology
Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel +4
A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis v1.0, the first large-scale c…
Phone Features Improve Speech Translation
Elizabeth Salesky, Alan W Black
End-to-end models for speech translation (ST) more tightly couple speech recognition (ASR) and machine translation (MT) than a traditional cascade of separate ASR and MT models, wi…
Generalized Entropy Regularization or: There's Nothing Special about Label Smoothing
Clara Meister, Elizabeth Salesky, Ryan Cotterell
Prior work has explored directly regularizing the output distributions of probabilistic models to alleviate peaky (i.e. over-confident) predictions, a common sign of overfitting. T…
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
Aditi Chaudhary, Elizabeth Salesky, Gayatri Bhat +3
This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context. This task requires us to produce the lem…
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
Elizabeth Salesky, Matthias Sperber, Alan W Black
Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show th…
Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Elizabeth Salesky, Matthias Sperber, Alex Waibel
Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech tra…
Towards Fluent Translations from Disfluent Speech
Elizabeth Salesky, Susanne Burger, Jan Niehues +1
When translating from speech, special consideration for conversational speech phenomena such as disfluencies is necessary. Most machine translation training data consists of well-f…
Optimizing Segmentation Granularity for Neural Machine Translation
Elizabeth Salesky, Andrew Runge, Alex Coda +2
In neural machine translation (NMT), it is has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair…