​​BERT-of-Theseus: Compressing BERT by Progressive Module Replacing

tl;dr
[ONE loss] + [ONE hyperparameter] + [NO external data] = GREAT PERFORMANCE
with a huggingface – compatible weights

take original BERT, replace some of his layers with new (smaller) ones randomly during the distillation. the probability of replacing the module will increase over time, resulting in a small model at the end.
them approach leverages only one loss function and one hyper-parameter, liberating human effort from hyper-parameter tuning.
also, they outperform existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression

paper: https://arxiv.org/abs/2002.02925
github: https://github.com/JetRunner/BERT-of-Theseus

#nlp #compressing #knowledge #distillation #bert

​​Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

The authors propose the first large-scale language VAE model – Optimus.

This new model uses BERT weights in the encoder and GPT-2 weights in the decoder. Thanks to this Optimus supports NLU and text generation tasks. Learned language representation is more universal, which means that it is easier to fine-tune this model to a new domain/task. Also, Optimus can control high-level semantics in text generation (tense, topic, sentiment).

There are several novel contributions, which are made thanks to this work:
– latent vector injection: two schemes are suggested to inject conditioning vectors into GPT-2 without retraining it;
– the idea to combine BERT and GPT-2 could inspire people to integrate existing language models into larger and ever more complex models;
– pre-training on a big corpora is an effective approach to reduce KL vanishing;
– VAE is a good approach to balance the compactness and usability of learned representations;
– pre-training latent space improves performance on several language tasks;

Experimental results on a wide range of tasks and datasets have demonstrated the strong performance of OPTIMUS, including new state-of-the-art for language VAEs.


Paper: https://arxiv.org/abs/2004.04092v1
Github: https://github.com/ChunyuanLI/Optimus

#deeplearning #nlp #nlu #transformer #vae #bert #gpt2

​​Transformer Reasoning Network for Image-Text Matching and Retrieval

A new approach for image-text matching using Faster-RCNN Bottom-Up and BERT.

Usually, downstream applications use the ResNet or one of its variants as the backbone CNN. Its simple and modular design can be easily adapted to various tasks. However, since ResNet models are originally designed for image classification, they may not be suitable for various downstream applications because of the limited receptive-field size and lack of cross-channel interaction.

Authors suggest an architecture, where images and texts are processed at first, and then their representations are combined.

Main contributions of the paper:
- TERN Architecture
- NDCG metric in addition to Recall@K
- show SOTA result on the benchmark


Paper: https://arxiv.org/abs/2004.09144
Code: https://github.com/mesnico/TERN

#computervision #deeplearning #bert #imagetextmatching

​​SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training

Authors introduce SpERT, an attention model for span-based joint entity and relation extraction.

This work investigates the use of Transformer networks for relation extraction: given a pre-defined set of target relations and a sentence such as “Leonardo DiCaprio starred in Christopher Nolan’s thriller Inception”, the goal is to extract triplets such as (“Leonardo DiCaprio”, Plays-In, “Inception”) or (“Inception”, Director, “Christopher Nolan”).

The main contributions of the paper are:
– a novel approach towards span-based joint entity and relation extraction
– ablation study showing that negative samples from the same sentence yield efficient training, a localized context representation is beneficial, finetuning a pre-trained model yields a strong performance increase over training from scratch.

This approach improves the SOTA score on CoNLL04 dataset by 2.6% (micro) F1.


Paper: https://arxiv.org/abs/1909.07755
Code: https://github.com/markus-eberts/spert

#nlp #deeplearning #transformer #bert #ner #relationextraction

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​​Scaling Transformer to 1M tokens and beyond with RMT

Imagine extending the context length of BERT, one of the most effective Transformer-based models in natural language processing, to an unprecedented two million tokens! This technical report unveils the Recurrent Memory Transformer (RMT) architecture, which achieves this incredible feat while maintaining high memory retrieval accuracy.

The RMT approach enables storage and processing of both local and global information, allowing information flow between segments of the input sequence through recurrence. The experiments showcase the effectiveness of this groundbreaking method, with immense potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.

Paper link: https://arxiv.org/abs/2304.11062
Code link: https://github.com/booydar/t5-experiments/tree/scaling-report

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-rmt-1m

#deeplearning #nlp #bert #memory