Hi! I'm Om, a senior-year engineering student specializing in Artificial Intelligence and Data Science. Recently, I've been interested in how words can convince people to think or do certain things. It's like text becomes more than just words on a page - it becomes a way to persuade others. As I learn more about this, I'm trying to figure out all the little details about how persuasive text works online.

Understanding Persuasion in Online Text:

One of the central themes of my current interest research revolves around the ways that we might be able to automatically detect the intent behind that persuasion. How do we detect who the text is targeting and what makes certain persuasive techniques more effective than others? These questions make me wonder how we can teach computers to understand the sneaky tricks people use to persuade others.

The Challenge of Bias in NLP Models:

One of the most pressing issues in NLP research is the presence of bias in trained models. These models, trained on vast amounts of online text, often reflect and even amplify societal biases. Yeah, it can get pretty tricky when you're training a model using a bunch of text from the internet. Sometimes, the model ends up saying stuff that's not cool - it might be uncomfortable, inappropriate, or even biased. Have you ever had that happen with your model? Fixing this problem is tricky and needs both looking at examples and using numbers to find and fix biases in the models.

Ok so let's assume, to tackle our problem we found common phrases or words that are used more often than the other. Then we can compare those to find the pattern in the language right?

But here's the tricky part: even if we find these patterns, it doesn't guarantee that the model won't mess up in a specific case. That's why it's important for the people creating these systems to be honest about the risks involved. Users should know what the model might do so they can decide if it's too risky for what they need.

How do you envision measuring and mitigating bias in NLP models, considering the complexity of linguistic biases embedded in training data?

And when to employ deep learning versus traditional techniques. Should we rely on deep learning models, or go with more interpretable methods such as lexicons and template matching? What factors do you believe should guide the decision between using deep learning and traditional techniques in NLP research?