Recent work has shown that neural models canbe successfully trained on multiple languagessimultaneously. We investigate whether suchmodels learn to share and exploit commonsyntactic knowledge among the languages onwhich they are trained. This extended abstractpresents our preliminary result
How cross-linguistically applicable are NLP models, specifically language models? A fair comparison ...
Multilingual pre-trained language models perform remarkably well on cross-lingual transfer for downs...
Large pretrained multilingual models, trained on dozens of languages, have delivered promising resul...
Recent work has shown that neural models canbe successfully trained on multiple languagessimultaneou...
The success of multilingual pre-trained models is underpinned by their ability to learn representati...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
NLP systems typically require support for more than one language. As different languages have differ...
Abstract: The subject area of multilingual natural language processing (NLP) is concerned with the p...
A neural language model trained on a text corpus can be used to induce distributed representations o...
The current generation of neural network-based natural language processing models excels at learning...
peer reviewedIn recent years, pre-trained Multilingual Language Models (MLLMs) have shown a strong a...
Recent research has shown promise in multilingual modeling, demonstrating how a single model is capa...
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven ...
Neural machine translation is known to require large numbers of parallel training sentences, which g...
A neural language model trained on a text corpus can be used to induce distributed representations o...
How cross-linguistically applicable are NLP models, specifically language models? A fair comparison ...
Multilingual pre-trained language models perform remarkably well on cross-lingual transfer for downs...
Large pretrained multilingual models, trained on dozens of languages, have delivered promising resul...
Recent work has shown that neural models canbe successfully trained on multiple languagessimultaneou...
The success of multilingual pre-trained models is underpinned by their ability to learn representati...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
NLP systems typically require support for more than one language. As different languages have differ...
Abstract: The subject area of multilingual natural language processing (NLP) is concerned with the p...
A neural language model trained on a text corpus can be used to induce distributed representations o...
The current generation of neural network-based natural language processing models excels at learning...
peer reviewedIn recent years, pre-trained Multilingual Language Models (MLLMs) have shown a strong a...
Recent research has shown promise in multilingual modeling, demonstrating how a single model is capa...
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven ...
Neural machine translation is known to require large numbers of parallel training sentences, which g...
A neural language model trained on a text corpus can be used to induce distributed representations o...
How cross-linguistically applicable are NLP models, specifically language models? A fair comparison ...
Multilingual pre-trained language models perform remarkably well on cross-lingual transfer for downs...
Large pretrained multilingual models, trained on dozens of languages, have delivered promising resul...