Search Results for author:
Found 13 papers, 6 papers with code
Subword-Delimited Downsampling for Better Character-Level Translation
This new downsampling method not only outperforms existing downsampling methods, showing that downsampling characters can be done without sacrificing quality, but also leads to promising performance compared to subword models for translation.
Patching Leaks in the Charformer for Efficient Character-Level Generation
Character-based representations have important advantages over subword-based ones for morphologically rich languages.
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing (NLP) tasks.
Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language
Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE$\rightarrow$DSB by 2. 76 BLEU.
Neural Machine Translation for English--Kazakh with Morphological Segmentation and Synthetic Data
This paper presents the systems submitted by the University of Groningen to the English{--} Kazakh language pair (both translation directions) for the WMT 2019 news translation task.
Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution
Unsupervised Machine Translation has been advancing our ability to translate without parallel data, but state-of-the-art methods assume an abundance of monolingual data.
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Translating to and from low-resource polysynthetic languages present numerous challenges for NMT.
Data Selection for Unsupervised Translation of German–Upper Sorbian
This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2020 Unsupervised Machine Translation task for German–Upper Sorbian.
Unsupervised Translation of German–Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language
This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German–Lower Sorbian (DE–DSB): a high-resource language to a low-resource one.
The Importance of Context in Very Low Resource Language Modeling
This paper investigates very low resource language model pretraining, when less than 100 thousand sentences are available.
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations
This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics.
LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification
Misogyny and sexism are growing problems in social media.
Too Much Information: Keeping Training Simple for BabyLMs
This paper details the work of the University of Groningen for the BabyLM Challenge.
