Recently, I came across a paper that announces a dataset release. The dataset is called PuzzLing and collects translation puzzles from the international linguistic olympiad. In machine translation jargon, I would say it provides extremely small training data to learn how to translate unknown languages. The title of the paper is PuzzLing Machines: A Challenge on Learning From Small Data, it has authors from the Technical University of Darmstadt and the University of Copenhagen and will be published on this year’s (virtual) ACL.
The paper is motivated by a dichotomy between two modes of thought discussed in Thinking Fast and Slow by Daniel Kahneman, a best-seller popular book on behavioral psychology. It distinguishes between System 1 thinking which is fast, instinctive, emotional, based on associations; and System 2 thinking which is slower, more deliberative, and more rational. System 2 is more expensive to use and therefore we use it only when necessary.
Current neural networks might do something similar to what the book attributes to System 1 thinking. We often interpret hidden layer activations as a result of measuring the similarity of the input with some “prototypes” learned from the data. This looks pretty much like associative thinking. After dozens of layers of measuring similarities of similarities (associations of associations), the networks can do quite complex decisions.
Kahneman’s book shows plenty of experiments that supposed to show people often do not make rational decisions (and by rational, he means that they do not maximize some utility). Most of the examples were word problems that included gambling and probabilities. I tend to interpret many of the examples linguistically as not resolving compositionality in the sentences: as if the experiment subjects only did some local keyword spotting instead of proper reading (whatever it is). My private conclusion from this was that most language understanding can be managed by fast associative thinking and only occasionally, it requires deeper thinking, and sometimes people do not really notice they should think slow.
The paper introducing the dataset argues that with the current NLP tasks and test sets, we cannot say whether our models only simulate capabilities of the System 1 thinking or if they can things we attribute to System 2 thinking as well (assuming System 2 thinking is necessary for NLP). To show this, they used translation puzzles from the International Linguistic Olympiad. In the Olympiad, the contestants are given a small set of translation examples between English and some not widely spoken language from the other side of the globe. They should use the examples to infer regularities in the languages and translate several other sentences. The examples are chosen such that there are no obvious analogies (no System 1 thinking), but they should be enough to uncover some complex underlying rules. We can say they are System-2-only puzzles.
The authors hope that people will share their assumptions and consider this task attractive. The other hope is that when solving this toy task, they can come up with something of general use. I am quite skeptical about this because I am not really sure if solving this task requires something that is conceptually necessary for solving some not-so-toy tasks. I cannot think of any real-world task that would be System-2-only (even AI for solving Sherlock Holmes gamebook is probably not the case).
Do we really need to explicitly simulate System 2 thinking to learn to translate well? If yes, is there really no workaround? Behavioral psychology tries to describe how human beings think (or at least how they do some cognitive tasks). Is there any reason to believe that the findings of behavioral psychology should provide guidelines on how should computers proceed when performing similar tasks? After all, airplanes do not fly like birds, why should computers think fast and slow.
Anyway, I am glad that the dataset was created and I am glad to see the powerful NLP models to fail on this dataset. It gives the community an excellent material for discussion whether the System 2 thinking capability is necessary for NLP models. (And I say probably not.) By the way, what would it mean if a pre-trained Transformer worked well on this task? Such a discussion is after all much more interesting than competing who will get more BLEU points on WMT test sets.