Universal Language Model Fine-tuning for Text Classification
We propose Universal Language Model. Fine-tuning (ULMFiT) an effective trans- fer learning method that can be applied to any task in NLP
A Neural Probabilistic Language Model
A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because
Cross-lingual Language Model Pretraining
We show that cross-lingual language models can provide significant improvements on the perplexity of low-resource languages. 5. We make our code and pretrained
Variable-Length Sequence Language Model for Large Vocabulary
19 oct. 2006 One of the most successful language models used in speech recognition is the n-gram model which assumes that the statistical dependencies ...
When Being Unseen from mBERT is just the Beginning: Handling
Handling New Languages With Multilingual Language Models. Benjamin Muller†*. Antonios Anastasopoulos‡ tation experiments to get usable language model-.
Enhancing lexical cohesion measure with confidence measures
30 nov. 2011 Enhancing lexical cohesion measure with confidence measures semantic relations and language model interpolation for multimedia spoken con-.
Release of largest trained open-science multilingual language
12 juil. 2022 BLOOM is the largest multilingual language model to be trained 100% openly and transparently. AI models of its kind.
Recurrent Neural Network Based Language Model
A new recurrent neural network based language model (RNN. LM) with applications to speech recognition is presented. Re- sults indicate that it is possible
N-gram Language Models
language model. LM ities to sentences and sequences of words the n-gram. An n-gram is a sequence n-gram of n words: a 2-gram (which we'll call bigram) is a
