Forum: Artificial languages
Topic: How deep you are involved with Natural Language Processing (NLP)
Poster: Joel Pina Diaz
Among several, the neural network-based technique for natural language processing (NLP) pre-training, called Bidirectional Encoder Representations from Transformers, or "BERT", is changing your models to search either from ranking and/or featured snippets cracking your queries mostly in US English but near future will add several languages (a market open for translators).
"One of the biggest challenges in natural language processing (NLP) is the shortage of training data. Because NLP is a diversified field with many distinct tasks, most task-specific datasets contain only a few thousand or a few hundred thousand human-labeled training examples. However, modern deep learning-based NLP models see benefits from much larger amounts of data, improving when trained on millions, or billions, of annotated training examples. To help close this gap in data, researchers have developed a variety of techniques for training general purpose language representation models using the enormous amount of unannotated text on the web (known as pre-training). The pre-trained model can then be fine-tuned on small-data NLP tasks like question answering and sentiment analysis, resulting in substantial accuracy improvements compared to training on these datasets from scratch..."
Language understanding remains an ongoing challenge and we are in the trend to ride up...
Full information in the following link (Google A.I. Blog): [url removed]
Topic: How deep you are involved with Natural Language Processing (NLP)
Poster: Joel Pina Diaz
Among several, the neural network-based technique for natural language processing (NLP) pre-training, called Bidirectional Encoder Representations from Transformers, or "BERT", is changing your models to search either from ranking and/or featured snippets cracking your queries mostly in US English but near future will add several languages (a market open for translators).
"One of the biggest challenges in natural language processing (NLP) is the shortage of training data. Because NLP is a diversified field with many distinct tasks, most task-specific datasets contain only a few thousand or a few hundred thousand human-labeled training examples. However, modern deep learning-based NLP models see benefits from much larger amounts of data, improving when trained on millions, or billions, of annotated training examples. To help close this gap in data, researchers have developed a variety of techniques for training general purpose language representation models using the enormous amount of unannotated text on the web (known as pre-training). The pre-trained model can then be fine-tuned on small-data NLP tasks like question answering and sentiment analysis, resulting in substantial accuracy improvements compared to training on these datasets from scratch..."
Language understanding remains an ongoing challenge and we are in the trend to ride up...
Full information in the following link (Google A.I. Blog): [url removed]
