Yousef El-Kurdi

Also published as: Yousef El-kurdi


Zero-Shot Dynamic Quantization for Transformer Inference
Yousef El-kurdi | Jerry Quinn | Avi Sil
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure, or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset.Our method permits taking advantage of quantization without the need for these adjustments.We present results on several NLP tasks demonstrating the usefulness of this technique.


Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers
Yousef El-Kurdi | Hiroshi Kanayama | Efsun Sarioglu Kayi | Vittorio Castelli | Todd Ward | Radu Florian
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. The synthetic treebanks are generated using a state-of-the-art biaffine parser adapted with pretrained Transformer models, such as Multilingual BERT (M-BERT). The new parser improves LAS by up to two points on seven languages. The production models’ LAS performance improves as the augmented treebanks scale in size, surpassing performance of production models trained on originally annotated UD treebanks.


Multi-Granular Text Encoding for Self-Explaining Categorization
Zhiguo Wang | Yue Zhang | Mo Yu | Wei Zhang | Lin Pan | Linfeng Song | Kun Xu | Yousef El-Kurdi
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction. In this work, we define multi-granular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams. We leverage the tree-structured LSTM to learn a context-independent representation for each unit via parameter sharing. Experiments on medical disease classification show that our model is more accurate, efficient and compact than the BiLSTM and CNN baselines. More importantly, our model can extract intuitive multi-granular evidence to support its predictions.