Aymen Souid

Build Voice Assistant and integrate Language Model to boost accuracy:

. Train Conformer architecture to build robust ASR system for French, Arabic, Turkish and Hebrew languages.

. Training models that handle multi-lingual complexities

. Build tokenizers for rich morphological languages.

• Experiment with different n-gram orders to find the optimal balance between accuracy and

computational cost.

• Use techniques like interpolation, ngram shrinking, pruning, and model interpolation to improve performance. Improve language model scores and perplexity:

• Employ data augmentation techniques such as backtranslation and noise injection. Minimize word error rate (WER) and sentence error rate (SER):

• Utilize beam search decoding, lattice rescoring with language models, and confidence estimation.

• Experiment with different token lexicon patches to improve the accuracy of word recognition. Optimize text-to-speech systems:

• Use pretrained models to recover diacritics for languages like Arabic. Create statistical tokenizers:

• Implement techniques like character-based, word-based, and subword tokenization.

• Consider using OpenNLP tokenizer for languages like Arabic. Resolve client bug tickets:

• Conduct thorough investigations to identify root causes.

• Debug code and implement appropriate fixes.

Implement research paper solutions:

• Explore neural machine translation models, self-supervised learning techniques, and transfer learning approaches..

Tasks:

• Scraping without being flagged as a bot using rotating proxies approaches

• Developing scripts and code to automate the data extraction process, and handling data in a way that is accurate by connecting spiders with Django views.

• Train classificationsNLP model for topic modeling using Pytorch

• Serve Machine Learning models to production using TorchScript and TorchServe

• Use the GPT3 model for data collection and augmentation to generate meaningful queries from random

keywords.

• Dockerise microservices cloud platform Cloud Run service.

• custom NLP models on Vertex AI (Batch prediction service)

• Automate deployment process using terraform .

• Design and configure database for the backend part using Django Google

Media loves tech as a Media loves tech is a competition where teams come up with an innovative ideas and try to realize, founded DW Akademie and Al Khatt: https://www.facebook.com/MediaLovesTech/ Information as I participated to brainstorm about a prototype to design a fake news detection application in the MENA region and especially about language specific tools for knowledge production and data analysis.