Edward Gow-Smith
9 papers · 2021–2025 · 5 venues · across top CS/AI venues
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Achievements
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🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (18) 🐣 Hot Topic Early Bird 👥 Mega-Team · Squad
🔥
Unstoppable · Bronze
Top co-authors
Research topics
Machine Learning
(6)
Natural Language Processing
(5)
Artificial Intelligence
(3)
Deep Learning
(3)
Applications
(3)
Core AI
(3)
Learning Types · Machine Learning
(3)
Resources & Methods
(3)
Application Areas
(2)
Architectures
(2)
Core Methods
(2)
Understanding
(2)
Interdisciplinary
(1)
Generation
(1)
Learning Types · Deep Learning
(1)
Keywords
low-resource language
(4)
machine translation
(3)
idiom detection
(2)
multilingual model
(2)
language model
(1)
lexical semantics
(1)
binary classification
(1)
semantic analysis
(1)
domain adaptation
(1)
named entity recognition
(1)
subword tokenization
(1)
pre-trained model
(1)
parameter-efficient fine-tuning
(1)
evaluation metric
(1)
pre-trained language model
(1)
neural machine translation
(1)
zero-shot prompting
(1)
transformer encoder
(1)
sentence embedding
(1)
transfer learning
(1)
Papers
Findings of the AmericasNLP 2025 Shared Tasks on Machine Translation, Creation of Educational Material, and Translation Metrics for Indigenous Languages of the Americas
NAACL 2025
Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole
NAACL 2025
Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
COLING 2024
Word Boundary Information Isn’t Useful for Encoder Language Models
ACL 2024
NAVER LABS Europe’s Multilingual Speech Translation Systems for the IWSLT 2023 Low-Resource Track
ACL 2023
Sheffield’s Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages
ACL 2023
Improving Tokenisation by Alternative Treatment of Spaces
EMNLP 2022
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
SemEval 2022
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
EMNLP 2021