AI Technology & Industry Review
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
Amazon extracts an optimal subset of architectural parameters for BERT architecture by applying recent breakthroughs in algorithms for neural architecture search.
AMBERT (A Multigrained BERT) leverages both fine-grained and coarse-grained tokenizations to achieve SOTA performance on English and Chinese language tasks.
Researchers dive deep into the large language model to discover how it encodes the structured commonsense knowledge it leverages on downstream commonsense tasks.
TaBERT-powered neural semantic parsers showed performance improvements on the challenging benchmark WikiTableQuestions and demonstrated competitive performance on the text-to-SQL dataset Spider.
Researchers add syntactic biases to determine whether and where they can help BERT achieve better understanding.
Google Research team proposes the automatic metric BLEURT which is based on the highly successful Google language model BERT.
Researchers from Bocconi University have prepared an online overview of the commonalities and differences between language-specific BERT models and mBERT.
Researchers propose a novel model compression approach to effectively compress BERT by progressive module replacing.
A recent Google Brain paper looks into Google’s hugely successful transformer network — BERT — and how it represents linguistic information internally.
Facebook AI researchers have further developed the BART model with the introduction of mBART.
A group of researchers from The Katholieke Universiteit Leuven and The Technical University of Berlin recently introduced a Dutch RoBERTa-based language model, RobBERT.
Google has now released a major V2 ALBERT update and open-sourced Chinese ALBERT models.
Now, a team from Facebook AI Research, Inria, and Sorbonne Université have released CamemBERT, essentially a French version of Google AI’s game-changing pretrained language model BERT.
Synced Global AI Weekly October 27th
In what the company calls “the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search,” Google today announced that it has leveraged its pretrained language model BERT to dramatically improve the understanding of search queries.
Researchers from the Huazhong University of Science and Technology and Huawei Noah’s Ark Lab have introduced TinyBERT, a smaller and faster version of Google’s popular large-scale pre-trained language processing model BERT.
Since Google Research introduced its Bidirectional Transformer (BERT) in 2018 the model has gained unprecedented popularity among researchers. Now, a group of researchers from the National Cheng Kung University Tainan in Taiwan are challenging BERT’s efficacy.
While it is exhilarating to see AI researchers pushing the performance of cutting-edge models to new heights, the costs of such processes are also rising at a dizzying rate.
Github developer Hugging Face has updated its repository with a PyTorch reimplementation of the GPT-2 language model small version that OpenAI open-sourced last week, along with pretrained models and fine-tuning examples.