
Hugging Face Blog
· 1 min read
How Hugging Face Accelerated Development of Witty Works Writing Assistant
The Success Story of Witty Works with the Hugging Face Expert Acceleration Program.
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Business Context
As IT continues to evolve and reshape our world, creating a more diverse and inclusive environment within the industry is imperative. Witty Works was built in 2018 to address this challenge. Starting as a consulting company advising organizations on becoming more diverse, Witty Works first helped them write job ads using inclusive language. To scale this effort, in 2019, they built a web app to assist users in writing inclusive job ads in English, French and German. They enlarged the scope rapidly with a writing assistant working as a browser extension that automatically fixes and explains potential bias in emails, Linkedin posts, job ads, etc. The aim was to offer a solution for internal and external communication that fosters a cultural change by providing micro-learning bites that explain the underlying bias of highlighted words and phrases.
Example of suggestions by the writing assistant
First experiments
Witty Works first chose a basic machine learning approach to build their assistant from scratch. Using transfer learning with pre-trained spaCy models, the assistant was able to:
- Analyze text and transform words into lemmas,
- Perform a linguistic analysis,
- Extract the linguistic features from the text (plural and singular forms, gender), part-of-speech tags (pronouns, verbs, nouns, adjectives, etc.), word dependencies labels, named entity recognition, etc.
By detecting and filtering words according to a specific knowledge base using linguistic features, the assistant could highlight non-inclusive words and suggest alternatives in real-time.
Challenge
Example of context dependent non-inclusive words:
Fossil fuels are not renewable resources. Vs He is an old fossil
You will have a flexible schedule. Vs You should keep your schedule flexible.
Original source
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