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Found 12 papers, 4 papers with code
Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months
We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model.
Building Domain-specific Corpora from the Web: the Case of European Digital Service Infrastructures
An important goal of the MaCoCu project is to improve EU-specific NLP systems that concern their Digital Service Infrastructures (DSIs).
An English-Swahili parallel corpus and its use for neural machine translation in the news domain
This paper describes our approach to create a neural machine translation system to translate between English and Swahili (both directions) in the news domain, as well as the process we followed to crawl the necessary parallel corpora from the Internet.
Bicleaner at WMT 2020: Universitat d’Alacant-Prompsit’s submission to the parallel corpus filtering shared task
This paper describes the joint submission of Universitat d’Alacant and Prompsit Language Engineering to the WMT 2020 shared task on parallel corpus filtering.
MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages
We introduce the project “MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages”, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages.
Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages
Large, curated, web-crawled corpora play a vital role in training language models (LMs).
Non-Fluent Synthetic Target-Language Data Improve Neural Machine Translation
When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them.
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation
The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way.
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences.
Data Augmentation
Low-Resource Neural Machine Translation
+3
Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort.
UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
We describe the Universitat d'Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018.
