AI Technology & Industry Review
In a new paper Exponentially Faster Language Modelling, an ETH Zurich research team introduces UltraFastBERT, a variant of the BERT architecture. UltraFastBERT takes a revolutionary approach by replacing feedforward layers with fast feedforward networks, resulting in an impressive 78x speedup over the optimized baseline feedforward implementation.
In the new paper Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling, a research team from Peking University, ByteDance, and the University of Oxford presents Sparse Masked Modelling with Hierarchy (SparK), the first BERT-style pretraining approach that can be used on convolutional models without any backbone modifications.
A research team from Carnegie Mellon University and Google systematically explores strategies for leveraging the relatively under-studied resource of bilingual lexicons to adapt pretrained multilingual models to low-resource languages. Their resulting Lexicon-based Adaptation approach produces consistent performance improvements without requiring additional monolingual text.
In the new paper Token Dropping for Efficient BERT Pretraining, a research team from Google, New York University, and the University of Maryland proposes a simple but effective “token dropping” technique that significantly reduces the pretraining cost of transformer models such as BERT without hurting performance on downstream fine-tuning tasks.
Microsoft releases DeBERTaV3, improving the original DeBERTa model using ELECTRA-style pretraining with gradient-disentangled embedding sharing to achieve better pretraining efficiency and a significant performance jump.
An Intel research team presents Prune Once for All (Prune OFA), a training method that leverages weight pruning and model distillation to produce pretrained transformer-based language models with high sparsity ratios. Applied to BERT, the approach achieves state-of-the-art results in compression-to-accuracy ratio.
A research team from ByteDance, Johns Hopkins University, Shanghai Jiao Tong University and UC Santa Cruz seeks to apply the proven technique of masked language modelling to the training of better vision transformers, presenting iBOT (image BERT pretraining with Online Tokenizer), a self-supervised framework that performs masked prediction with an online tokenizer.
A Huawei research team proposes a topic-based personalized ranking model (TPRM) that integrates pretrained contextualized term representations and user profiles constructed by a topic model to tailor the output ranking list.
A research team from Google shows that replacing transformers’ self-attention sublayers with Fourier Transform achieves 92 percent of BERT accuracy on the GLUE benchmark with training times seven times faster on GPUs and twice as fast on TPUs.
A research team from Huawei Noah’s Ark Lab and Tsinghua University proposes Extract Then Distill (ETD), a generic and flexible strategy for reusing teacher model parameters for efficient and effective task-agnostic distillation that can be applied to student models of any size.
A research team from Google Research proposes small, fast, on-device disfluency detection models based on the BERT architecture. The smallest model size is only 1.3 MiB, representing a size reduction of two orders of magnitude and an inference latency reduction of a factor of eight compared to state-of-the-art BERT-based models.
An IBM research team proposes four multilingual adversarial attack strategies and attacks seven languages in a zero-shot setting on large multilingual pretrained language models (e.g. MBERT), reducing average performance by up to 85.6 percent.
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.