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9 Nov 2023Explore the crucial role of language data in training and fine-tuning LLMs and GenAI, ensuring high-quality, context-aware translations, fostering the symbiosis of human and machine in the localization sector.
19 Dec 2022Domain Adaptation can be classified into three types - supervised, semi-supervised, and unsupervised - and three methods - model-centric, data-centric, or hybrid.
19 Dec 2022Machine learning and AI applications need data in order to work. And in order to get good results and output, the cleaner the data, the better.
19 Dec 2022Text Summarization can be categorized under two types: Extraction and Abstraction. With the power of AI, summarization is becoming more popular and accessible.
7 Oct 2022Synthetic parallel data generation by back-translation as a solution for the problem of translating low-resource languages and texts from low-resource domains.
by Şölen Aslan3 Mar 2022Natural Language Technologies are on the rise: making optimal use of NLT and its subcategories is crucial to remain up-to-date with the latest AI solutions
3 Jan 2022What can word clouds driven by NLP tell you about your training datasets? Here is how we create word clouds on TAUS Data Marketplace.
2 Dec 2021The next logical translation solution: Data Enhanced Machine Translation (DEMT)
by Şölen Aslan1 Dec 2021Which language data for AI trends you should expect to rise in 2022: expansion of multilingual AI data and models, more companies joining the data market, data diversity and lifelong learning machines.
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by Husna Sayedi18 Nov 2021A thorough overview of the paper by six Google researchers: Data Cascades in High-Stakes AI with a focus on why data-centric AI matters.
by Husna Sayedi4 Nov 2021Explaining what Explainable AI (XAI) entails and diving into five major XAI techniques for Natural Language Processing (NLP).
by Husna Sayedi4 Oct 2021Reasons why training data is important for AI and ML practices.
by Husna Sayedi4 Oct 2021A brief introduction to types of training data including structured, unstructured, and semi-structured data.
by Husna Sayedi4 Oct 2021Here are some pointers on how much training data do you need to train your ML models.
by Husna Sayedi4 Oct 2021Data cleaning and data anonymization are very critical in training ML models. Here are the reasons why.
by Husna Sayedi4 Oct 2021Training data can be sourced via synthetic data generation, public datasets, data marketplaces, and crowd-sourced platforms.
1 Oct 2021Web scraping is a common way to generate parallel data, making use of the immense source of multilingual data offered on the web. Here is how to do web scraping.
by Husna Sayedi7 Sep 2021Definition and common use cases of intent recognition as an essential element of NLP.
19 Aug 2021A tutorial on automatic domain classification with NLP: from data preprocessing to the training and evaluation of an artificial neural network.