I wonder if this approach is extendable to learning to predict the next word from a corpus of texts...
The first layer might perhaps still be embedding from words to vectors, but what should one do then? What would be a possible minimum viable dataset?
Perhaps, in the spirit of PoC of the paper, one might consider binary sequences of 0s and 1s, and have only two words, 0 and 1, and ask what would it take to have a good predictor of the next 0 or 1 given a long sequence of those as a context. This might be a good starting point, and then one might consider different examples of that problem (different examples of (sets of) sequences of 0 and 1 to learn from).
These are interesting considerations! I haven’t put much thought on this yet but I have some preliminary ideas.
Semantic features are intended to capture meaning-preserving variations of structures. In that sense the “next word” problem seems ill-posed as some permutations of words preserve meaning; in reality its a hardly natural problem also from the human perspective.
The question I’d ask here is “what are the basic semantic building blocks of text for us humans?” and then try to model these blocks using the machinery of semantic features, i.e. model the invariants of these semantic blocks. Only then I’d think of adequate formulations of useful problems regarding text understanding.
So I’d say that these semantic atoms of text are actually thoughts (encoded by certain sequences of words/sentences that enjoy certain permutation-invariance). Thus semantic features would aim to capture thoughts-at-locations by finding these sequences (up to their specific permutations) and deeper layers would capture higher-level thoughts-at-locations composed of the former. This could potentially uncover some euclidian structure in the text (which makes sense as humans arguably think within the space-time framework, after Kant’s famous “Space and time are the framework within which the mind is constrained to construct its experience of reality”).
That being said the problems I’d consider would be some forms of translation (to another language or another modality) rather then the artificial next-word prediction.
The MVD for this problem could very well consist of 0′s and 1′s provided that they’d encode some simple yet sensible semantics. I’d have to think of a specific example more, it’s a nice question :)
In addition to translation (which I do think is a useful problem for theoretical experiments), I would recommend question answering as something which gets at ‘thoughts’ rather than distractors like ‘linguistic style’. I don’t think multiple choice question answering is all that great a measure for some things, but it is a cleaner measure of the correctness of the underlying thoughts.
I agree that abstracting away from things like choice of grammar/punctuation or which synonym to use is important to keeping the research question clean.
Thank you! The quote you picked is on point, I added an extended summary based on this, thanks for the suggestion!
Thanks, this is very interesting.
I wonder if this approach is extendable to learning to predict the next word from a corpus of texts...
The first layer might perhaps still be embedding from words to vectors, but what should one do then? What would be a possible minimum viable dataset?
Perhaps, in the spirit of PoC of the paper, one might consider binary sequences of 0s and 1s, and have only two words, 0 and 1, and ask what would it take to have a good predictor of the next 0 or 1 given a long sequence of those as a context. This might be a good starting point, and then one might consider different examples of that problem (different examples of (sets of) sequences of 0 and 1 to learn from).
These are interesting considerations! I haven’t put much thought on this yet but I have some preliminary ideas.
Semantic features are intended to capture meaning-preserving variations of structures. In that sense the “next word” problem seems ill-posed as some permutations of words preserve meaning; in reality its a hardly natural problem also from the human perspective.
The question I’d ask here is “what are the basic semantic building blocks of text for us humans?” and then try to model these blocks using the machinery of semantic features, i.e. model the invariants of these semantic blocks. Only then I’d think of adequate formulations of useful problems regarding text understanding.
So I’d say that these semantic atoms of text are actually thoughts (encoded by certain sequences of words/sentences that enjoy certain permutation-invariance). Thus semantic features would aim to capture thoughts-at-locations by finding these sequences (up to their specific permutations) and deeper layers would capture higher-level thoughts-at-locations composed of the former. This could potentially uncover some euclidian structure in the text (which makes sense as humans arguably think within the space-time framework, after Kant’s famous “Space and time are the framework within which the mind is constrained to construct its experience of reality”).
That being said the problems I’d consider would be some forms of translation (to another language or another modality) rather then the artificial next-word prediction.
The MVD for this problem could very well consist of 0′s and 1′s provided that they’d encode some simple yet sensible semantics. I’d have to think of a specific example more, it’s a nice question :)
In addition to translation (which I do think is a useful problem for theoretical experiments), I would recommend question answering as something which gets at ‘thoughts’ rather than distractors like ‘linguistic style’. I don’t think multiple choice question answering is all that great a measure for some things, but it is a cleaner measure of the correctness of the underlying thoughts.
I agree that abstracting away from things like choice of grammar/punctuation or which synonym to use is important to keeping the research question clean.