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I've worked on classical NLP models for quite some time, and this indeed looks way too simple to be of any practical use. If you mention Montague, I'm going to

by tgv 2y ago

I've worked on classical NLP models for quite some time, and this indeed looks way too simple to be of any practical use. If you mention Montague, I'm going to refer you to "Pedro owns a donkey," the poster kid sentence for Discourse Representation Theory. That's 1980s work, and for simple sentences it's already complicated beyond what the OP article suggests, and fails on anything remotely complex. I think it goes 2nd order the moment a complement is introduced (I think that ...). And even if you can translate a sentence into a predicate, you haven't begun understanding what lies behind all those predicates. E.g., "Zelensky is ready to work under Trump's 'strong leadership' after 'regrettable' showdown." What good does it do to have that in FOP? [1] https://plato.stanford.edu/archIves/sum2011/entries/discourse-representation-theory/ https://plato.stanford.edu/archIves/sum2011/entries/discours...

zozbot234 2y ago
It looks like classic models of NLP semantics mostly punt on the "logical" point of view precisely due to these difficulties, and focus mostly on the more surface level problem of describing how each word of the source text correlates with a deeper description of the "meaning" of the text as a whole. So it is simply assumed that the meaning of the text as a whole must be derived compositionally from the meaning of each part (usually described by a somewhat ad-hoc "frame" structure), but exactly what that entails in a "logical" sense is left unspecified. UMR (Universal Meaning Representations) seems to be a typical example of such a system https://github.com/umr4nlp/umr-guidelines/blob/master/guidelines.md https://github.com/umr4nlp/umr-guidelines/blob/master/guidel... The expected use case seems to be something like building a common intermediate language for an automated translation system; individual meaning elements can then be "mapped" in a useful way, even across different languages, but there's not much interest apparently in "inferring" further knowledge from what one already has, or even on verifying that any given inference is valid (as proposed by OP).
WaxProlix 2y ago
Even beyond that you have a ton of pragmatics post-grice to deal with. Computing implicatures is complex and requires a lot of knowledge about context etc. The truth value of a statement and the 'truth value' of a speech act are pretty different things - not sure it's really feasible to convert between them.

by tgv 2y ago

zozbot234 2y ago

It looks like classic models of NLP semantics mostly punt on the "logical" point of view precisely due to these difficulties, and focus mostly on the more surface level problem of describing how each word of the source text correlates with a deeper description of the "meaning" of the text as a whole. So it is simply assumed that the meaning of the text as a whole must be derived compositionally from the meaning of each part (usually described by a somewhat ad-hoc "frame" structure), but exactly what that entails in a "logical" sense is left unspecified. UMR (Universal Meaning Representations) seems to be a typical example of such a system https://github.com/umr4nlp/umr-guidelines/blob/master/guidelines.md https://github.com/umr4nlp/umr-guidelines/blob/master/guidel... The expected use case seems to be something like building a common intermediate language for an automated translation system; individual meaning elements can then be "mapped" in a useful way, even across different languages, but there's not much interest apparently in "inferring" further knowledge from what one already has, or even on verifying that any given inference is valid (as proposed by OP).

WaxProlix 2y ago

Even beyond that you have a ton of pragmatics post-grice to deal with. Computing implicatures is complex and requires a lot of knowledge about context etc. The truth value of a statement and the 'truth value' of a speech act are pretty different things - not sure it's really feasible to convert between them.