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Found 18 papers, 6 papers with code
Writing System and Speaker Metadata for 2,800+ Language Varieties
We describe an open-source dataset providing metadata for about 2, 800 language varieties used in the world today.
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation
Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT).
Building Machine Translation Systems for the Next Thousand Languages
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
While recent progress in massively multilingual MT is one step closer to reaching this goal, it is becoming evident that extending a multilingual MT system simply by training on more parallel data is unscalable, since the availability of labeled data for low-resource and non-English-centric language pairs is forbiddingly limited.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus
Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context.
Translationese as a Language in ``Multilingual'' NMT
Machine translation has an undesirable propensity to produce {``}translationese{''} artifacts, which can lead to higher BLEU scores while being liked less by human raters.
BLEU might be Guilty but References are not Innocent
The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems.
Translationese as a Language in "Multilingual" NMT
Machine translation has an undesirable propensity to produce "translationese" artifacts, which can lead to higher BLEU scores while being liked less by human raters.
Investigating Multilingual NMT Representations at Scale
Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings.
Learning a Multi-Domain Curriculum for Neural Machine Translation
Most data selection research in machine translation focuses on improving a single domain.
Dynamically Composing Domain-Data Selection with Clean-Data Selection by ``Co-Curricular Learning'' for Neural Machine Translation
Noise and domain are important aspects of data quality for neural machine translation.
Tagged Back-Translation
Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during back-translation, a method to generate synthetic parallel data.
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation
Noise and domain are important aspects of data quality for neural machine translation.
APE at Scale and its Implications on MT Evaluation Biases
In this work, we train an Automatic Post-Editing (APE) model and use it to reveal biases in standard Machine Translation (MT) evaluation procedures.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
