How can you communicate NLP project results to non-experts?

1. Simplify Technical Jargon: Avoid technical terms, and explain concepts in plain language. 2. Visual Aids: Utilize infographics, charts, and diagrams to illustrate key findings. 3. Real-World Examples: Provide relatable examples to demonstrate the project's impact. 4. Storytelling Approach: Frame the results as a narrative, highlighting the problem, solution, and outcomes. 5. Analogies: Use analogies from everyday life to explain complex NLP processes. 6. Engage in Dialogue: Encourage questions and discussions to ensure comprehension. 7. Summary Documents: Create concise summaries or executive briefs outlining the project's objectives, methodology, and conclusions.

One thing I've found helpful was to frame the explanation using a problem-solution based approach. First, using simple terms, we state the problem we are trying to solve, e.g., "understand if a text is fake news or not" and then, we enumerate the steps that will be taken to get to that solution "1. analysis of the level of emotion of the text", 2. detection of reliable sources in the text", ...5. have a probability for the text being fake news". It is a recursive strategy, because we can further go into step 1 and state the problem-solution "since fake news prey on emotion, we use sentiment analysis to identify the main emotion in the text, we do that with Python's NLTK library".

1. Talk Benefits & Not Tech: Explain the real-world problem your project solved. Did it improve customer service chats or automate document summarization/ finding insights etc. Focus on how it has improved productivity / cost savings etc. 2.Real-Life comparisons: Instead of saying "sentiment analysis", say understanding a customer's tone in their emails, Use relatable examples to bridge the gap 3. Visualize Your success: Charts, graphs, demos can showcase your project's capabilities. Seeing is believing, so replace technical terms with clear visuals 4. Keep it simple: No Jargons please but focus on key findings. Summarize what your project achieved in clear, concise language

I have been doing this for a while. When sending an email to a non- expert, Instead of: We used sentiment analysis to understand customer reviews. Try: We analyzed customer reviews to see if customers were generally happy or unhappy with the product. Instead of: We leveraged a targeted fine-tuning approach on the pre-trained NLP model to specialize it for customer service conversations. This entailed retraining on a corpus of labeled customer chat logs, enabling the model to refine its grasp of domain-specific language and enhance its precision in extracting semantic intent from customer interactions. Try: We improved the tool's understanding of customer conversations by retraining it with specific examples like real customer service chats.

Communicating NLP project results to non-experts involves highlighting the values in a clear and understandable manner. Start by emphasizing the practical benefits of the project, such as improving customer satisfaction, enhancing user experience, or increasing operational efficiency. Use simple language and real-world examples to illustrate how the NLP solution addresses specific pain points or challenges faced by the audience. Showcase tangible outcomes, such as cost savings, time efficiency, or revenue growth, to demonstrate the project's impact. Additionally, emphasize how the NLP solution aligns with broader organizational goals and objectives, emphasizing its value in driving business success.

In my experience, it is very important to address the project's limitations. Since there is still a lot of buzz and misinformation involving ML, sometimes people get the idea that the techniques are magical and that "perfect" results are possible. It is good to have some kind of alignment of perception and explain why having 100% accuracy would not only be impossible, but also mean that the model would probably generalize poorly under true conditions, and why that isn't interesting. Also, that other metrics, even tailored ones, might be a better fit to really be able to measure results.

It is helpful to have the audience interacting with the model and seeing results first hand, but having quizzes or games might only be interesting in informal contexts.

One thing I've found helpful in a business client facing scenario is to have a meeting each 2-3 weeks, or each time a new project milestone is achieved, in other words, every time there is some new part of the process that can be shared. It seems to be easier for familiar audiences to keep tracking the progress and for unfamiliar audiences to understand what is being done in smaller steps.

Focus on the “what” and “why” instead of the “how”: Explain what the NLP project achieved and why it's important. Avoid jargon and technical terms like “neural networks” or “embeddings.” Use real-world examples and analogies: Relate your results to something your audience understands. For instance, if your project improved customer service chatbots, explain how it translates to happier customers and faster resolutions. Tailor your message to your audience: Consider their level of technical knowledge and what would be most relevant to them. For example, a presentation for executives might focus on ROI (Return on Investment), while a presentation for marketing might delve into how NLP can improve customer targeting.