> evaluating the output of some NLP system regarding the above tasks is subjective [...] However, language understanding does not admit any degrees of freedom. A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey

I'm not understanding how NLU would be inherently more objective than something like the given example of named-entity recognition.

> None of the above challenging semantic understanding functions can be ‘approximately’ or ‘probably’ correct – but absolutely correct. In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. In summary, then, true understanding of ordinary spoken language is quite a different problem from mere text (or language) processing where we can accept approximately correct results – results that are also correct with some acceptable probability.

Why would only absolute accuracy be acceptable? Even humans misinterpret things with some frequency. In many cases, the text just won't contain enough context for anyone to give anything more than an educated guess to the writer's intentions.

I'd further argue that language is lossy and thoughts aren't discrete on the scales relevant to language, such that even in the best of cases we don't interpret exactly the same thought as the speaker has in mind - just something that's approximately close enough to allow for useful communication.

> The equivalence between (machine) learnability (ML) and compressibility (COMP) has been mathematically established.

Hopefully the article doesn't take "learning can be viewed as compression of information" to mean "all that a trained model can do is compression of its inputs".

> Natural language understanding, on the other hand, and due to MTP, requires intelligent ‘uncompressing’ techniques that would uncover all the missing and implicitly assumed text. Thus, machine learning and language understanding are incompatible – in fact, they are contradictory.

Aargh!

> Note that antonyms/opposites such as ‘small’ and ‘big’ (or ‘open’ and ‘close’, etc.) occur in the same contexts with equal probabilities. As such, (1a) and (1b) are statistically equivalent

If you're working with a Markov chain maybe.

> In ML/Data-driven approaches there is no type hierarchy where we can make generalized statements about a ‘bag’, a ‘suitcase’, a ‘briefcase’ etc. where all are considered subtypes of the general type ‘container’. Thus, each one of the above, in a purely data-driven paradigm, are different and must be ‘seen’ separately in the data. If we add to the semantic differences all the minor syntactic differences to the above pattern (say changing ‘because’ to ‘although’ — which also changes the correct referent to “it”) then a rough calculation tells us a ML/Data-driven system would need to see something like 40,000,000 variations of the above to learn how to resolve references in sentences such as (2).

Machine learning models don't need to see every possible combination to be able to generalise, and they can learn that bags/suitcases/etc. are related.

For this and the rest of the article, I keep feeling like I must be (ironically) misinterpreting the author. There's repeated bold theoretical claims about what machine learning will never do, but about tasks that modern models are already to some degree capable of.