On intuitions, Latin translations and prior distributions

Tue, 31 Aug 2021 07:14:58 -0400

Tags: philosophical, political, academic


Back in my teens, I attended a 7-year secondary school ("highschool"?) with a strong focus on humanities. Since I was 11 until I was 16, I had 5 hours a week of Latin instruction plus a similar amount of time spend in assignments doing translations at home. One of those years, the professor said "you can ask me anything, including why are we studying Latin". I duly obliged. He surprised us by saying teaching us how to do Latin translations was the humanities equivalent of teaching us Math; the way to teach structured thinking using words. Over time I started to see his point: translating languages with a strong case system was akin to solving a system of equations.

Fast forward a few years and I was finishing my undergraduate diploma thesis on Spanish parsing using Lexical Functional Grammars (LFG) where this whole "system of equations" come really to fruition. LFG poses a two-level process for parsing, the first one is a shallow process and the second one is (of course) a system of equations. But that still didn't quite capture my experience translating neverending diatribes against political adversaries. The equations were there, yes, but the solution was driven by intuitions on the roles and meanings of the words. At that time, I went to graduate school.

At Columbia, I got into statistical NLP and the intuitions I mentioned become clear as probability distributions, particularly as priors over the attachment of different words (is this word modifying the verb or the noun? well, if you have a large corpus of parsed text, you might find it usually modifies nouns and seldom verbs, so chances are in this case it also modifies the noun). The beauty of doing it by hand is connecting your (freshly being formed, in the case of my 12-year-old self) intuitions with those of somebody that has been dead for 20 centuries. Intuitions come from experience and the experience being shared goes beyond the text itself. But I disgress, the point here is that, as a human, we need intuitions to do the translation and in statistical systems, these are provided by statistics computed over the data.

In his book, "Thinking, Fast and Slow", Daniel Kahneman tells the story of firefighters trying to extinguish a fire in a kitchen, when the commander realizes something is wrong and gets everybody out before the floor collapses. Kahneman cites it as an example of intuition taking control. In our modern society, however,intuitions are somewhat shunned, because they are close to impossible to teach. "Do it until you develop enough intuitions to get better at it" does not sound like an actionable concept. In my book, I argue that feature engineering operations are intuition driven (otherwise they get folded into the actual machine learning algorithm). Intuitions both in the realm of general operations and also hyper-specific intuitions regarding the domain and dataset being worked on. Some of the criticism the book has received resembles my earlier comment and I feel the pain that teaching intuitions is hard.

It might all start with valuing intuitions more. North American society is not necessarily very keen on them. Latin Americans seem more connected to their intuitive selfs in general or at least "this feels right" can be considered a reasonable reason in my culture of origin. In the midtime, if you or your child were considering taking a Latin class, go for it. It teaches structured thinking and intuitions!


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