[nceasy]

Thank you very much! That's the type of answer I was expecting. Could you elaborate a little bit more on what kinda problem the industry is trying to solve with NLP today, even in the ecommerce space? That one you mentioned, about product review, is really interesting.

[kingcai]

Sure. I don't work in the ecommerce space, but I think the big problem that industry is attempting to solve right now is - "how can we take off the shelf language models and use them to do things that make us money?". This is a super broad problem and there's many answers to this question. It can be as complicated as creating intelligent chatbot, but also as simple as adding multi-lingual support to an app via cross-lingual training.

The example I gave of multi-modal learning was really just highlighting a dichotomy in the techniques that we use in machine learning today. FWIW I am a couple of years removed from working heavily with tabular data, so do take this with a grain of salt. But there are essentially two different modeling approaches for two different types of datasets. On the one hand, you have deep learning (BERT, language models, CV models) which does well on raw data like text or images. These usually work by mapping the raw data to dense embeddings, which are the output of neural models. On the other hand, you have decision trees / forests (think XG boost) that work great on tabular data - spreadsheets or other data of that nature.

But what do you do if you have a spreadsheet of data and one of the columns is raw text data but the other columns are say sparse boolean features? How can you incorporation the extra information from the spreadsheet into your language model? I think this is a common problem in industry that there's not a clear solution for right now.