Hacker News Reader

Re Linear B, it's also worth pointing out that prior to its decipherment the prevailing view was that it wasn't Greek; Ventris spent most of his time trying to align it to Etruscan, and only made the breakthrough when he tried out a few Greek words more or less on a whim. I imagine that had almost any form of computerised automation been readily available in the 50s it would have made the decipherment vastly simpler, but it would require an existing hypothesis of Greek; an AI trying to fit it to Etruscan would have been merely banging its head against an electronic wall.

> Well, I wish them good luck. But I don't know if ML can do any better than humans. It might be able to do what humans can faster, but I doubt it can do what we can't.

Being faster might be enough? Who knows, perhaps some human spending a few centuries in deep contemplation of Linear A might crack it?

It's not about deep contemplation, it's about having the necessary information.

If I write down a sentence in a language you don't speak, and make it impossible for you to ever get any information at all about the language it's written in (say, by burning every book about it and killing everyone who knows it), how many centuries of contemplation do you think it would take for your to understand it?

Language decipherment, whether by human or machine, is not done by staring at the words you don't understand. It's done by comparing it to things you do. And here we have nothing.

If you have lots of text and nothing else, deep contemplation can lead to understanding. This is far from the case here though, so you are right.

Not sure how? As a thought experiment, say I created two languages completely from scratch : symbols, words, sentence order, etc.

One of these languages, I've given meaning to the words, the other is random. However I create massive corpuses for each of hundreds of thousands of documents. Now go ahead and machine learn the hell out of both languages.

First question : which one is the 'random' language? Second question : what is the meaning of the non-random language texts? Assume I have not based my 'real' language on any existing example ...

If the machine or human is sufficiently intelligent and there is enough data and time, it is possible to do so as long as the non-random language is useful for human communication and the language sample describes some experiences or things we understand.

There are principles underlying all human languages. For example, they are used to describe the world or communicate intention and they must be understandable by a human.

Given that the copious data of the non-random language should describe something we understand about the world or social dynamics, we can try to match them with world/domain models and find patterns of plausible interpretations that fit.

The patterns should also satisfy the principle of communication economy and the limits of human cognition, such as the size of working memory. These constraints help narrow down the possibilities.

The human/AI system can test and figure out the most likely patterns first and use them to decipher more patterns.

All above is possible in principle but beyond the current state-of-the-art.

This is true, but you're forgetting that the sign (ie. pairing of an expression and its meaning) is arbitrary. There's no particular reason that the sound 'cow' (or the squiggles c-o-w when we write) refers to cows, and you can see that quite clearly by the fact that it only does so in one out of the Earth's 6-7000 current languages.

Note that this doesn't mean that relationships between words are arbitrary, they are of course highly systematic, else language wouldn't work. It also doesn't mean that language doesn't have history, the changes are also systematic. But if you take a random surface form (sound or writing), there is absolutely no way to relate it with any certainty to any content without a context that allows you to infer it.

You are being too pessimistic.

Yes, it's hard to figure out cow just from a big corpus; though you might learn enough about 'cow' to form meaningful sentences and answer questions. That's what GPT-3 is doing after all, and language models are still improving quickly.

And we do have context! Just knowing that a language is used by humans gives lots and lots of context. Human cultures and languages have more in common than you might realize.

For example, have a look at Deixis (https://en.wikipedia.org/wiki/Deixis):

> In linguistics, deixis (/ˈdaɪksɪs/, /ˈdeɪksɪs/)[1] is the use of general words and phrases to refer to a specific time, place, or person in context, e.g., the words tomorrow, there, and they. Words are deictic if their semantic meaning is fixed but their denoted meaning varies depending on time and/or place. Words or phrases that require contextual information to be fully understood—for example, English pronouns—are deictic. Deixis is closely related to anaphora. Although this article deals primarily with deixis in spoken language, the concept is sometimes applied to written language, gestures, and communication media as well. In linguistic anthropology, deixis is treated as a particular subclass of the more general semiotic phenomenon of indexicality, a sign "pointing to" some aspect of its context of occurrence.

> Although this article draws examples primarily from English, deixis is believed to be a feature (to some degree) of all natural languages.

I suspect with a large enough corpus you can most likely figure out the deixis of the given language.

See https://linguistics.stackexchange.com/a/4046 for some more structure shared with all human languages:

> However, all languages have some sort of a clause-type thing allowing them to express predication, attribution, etc. See Dixon's Basic Linguistic Theory: Basic Linguistic Theory Volume 1: Methodology . All languages also must have means of expressing cohesion and coherence (texture) although this is much less studied in cross linguistic perspective. Punctuated sentences are a kind of cohesive device.

(A comment on this answer points out that the reality is probabilistic, of course:)

> While I agree with you, it seems to me that linguists who study languages with a strong written tradition often talk about 'sentences', even when their examples are not from writing. Those of us who work on previously unwritten languages (I think) tend to talk about 'texts' or 'utterances'. I think this is an important issue as it relates to how some linguists have ignored the true messiness that is often found in examples of human language.

Of course, all of this refers to natural human languages. Not totally arbitrary constructs.

> Yes, it's hard to figure out cow just from a big corpus; though you might learn enough about 'cow' to form meaningful sentences and answer questions. That's what GPT-3 is doing after all, and language models are still improving quickly.

As I've said in a sibling comment, GPT-3 is able to construct new utterances in the same language that it learns from. It does not learn its language while learning a model of the world because it does not exist in the world, so it does not learn semantics like we do, which is requirement for making a translation. I am familiar with all these things you talk about, but you are not starting from a sound understanding of semiotics and the difference between content and expression.

Say you learn a lot of syntax from text, you are able to write out a list of paradigms. How do you know which verb form is past and which form is present? How do you know which word means day and which word means night? The information required to make that judgement is not present in the text artifact.

> How do you know which verb form is past and which form is present?

There's lots and lots of context. One interpretation will fit the data much better than another.

Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_.

I agree that all these connections are only probabilistic and you need enormous amounts of data.

Btw, GTP-3 can translate between English and French to certain very limited extent; but that's probably mostly because it has seen example translations.

To give you a testable prediction of my theory: if you trained something like GPT-3 on a corpus that includes examples of natural languages A and B, but never any piece of text that contains both languages, yet alone example translations, I would expect that nevertheless, the resulting network would share the same internal representation for the concept of 'cow' in both languages.

> Eg in spoken languages the equivalent of 'I' is used much more often than any other pronoun. Similarly simpler sentences are perhaps more likely to refer to the present than the past; eg past tense is much more likely to come with an additional specification of _when_ stuff happened. That information is less kind of a given when we are talking about _now_.

This still rests on a lot of assumptions about the language. Pronouns are used much more in some languages than others, some languages have more pronouns than others, etc.. Tense is not at all universal in the world's languages as you probably know. There are linguistic theories that even question the universality of nouns and verbs as categories, and even if they are to an extent universal, they are certainly not fixed in their boundaries across languages, e.g. most things that in English adjectives are expressed as verbs in Classical Arabic. Even if you could do a perfect job of extracting a set of syntactic categories and morphological paradigms from a language, I don't think you would be able to do anything with it. Word 3849 from category C often combines with words 201, 635, and 9913 from category F in conjugations 1-4, but never 5.

And of course, even for nouns that refer to basic, physical things, often some languages will have several words where another will make do with one, or have none. It quickly becomes a question of culture.

I don't think it would be very hard to detect which of the two is random, because meanings are likely to be somewhat correlated. Obviously you could model your 'random' language to be statistically similar to the meaningful one though, if you were feeling particularly evil, but then you'd be imbuing it with some small degree of meaning, arguably.

Whether you can go from this statistical analysis to anything with absolute meaning, I don't know. I suspect _some_ extremely common concepts (pronouns, verbs like to be or to have) might yield to this sort of direction.

But the meaning is not in the text (text-as-artifact, the ink on the page), it is strictly in the mind of the reader, accessed by reference to the speaker's experience with the language in question. If you don't have something to ground at least part of the symbols in, there is nothing to work from. Any word can be assigned any meaning, and there would be infinite possibilities. No amount of statistics will allow you to work out whether a given word or part of a word refers to a rock, a rabbit or Noam Chomsky's granddaughter. How could it? To be able to do this is called knowing a language, and if all the people who had that skill in the soft tissue of their brain have kicked the bucket, tough luck, you can't get that knowledge back.

All (bar none) decipherments of unknown writing systems have been done by reference to related languages, especially the existence of bilingual texts. Computers and statistics are definitely able to help us do that, but if all you have is a page of text with no context, then the information you're looking for is gone.

Actually, you can get a readout on lexical semantics (the meaning of words) if you have a large enough corpus (much larger than we have for any extinct language). The method uses a vector space of word cooccurrence (look up word2vec). It will tell you what words are semantically similar. What it won't tell you is what those words mean in the real world (unless possibly you have pictures with captions). It's like being able to see mountains in one direction and a lake in another and a valley in another direction; you can say you're going towards one or the other of these features, but (ignoring the sun and stars) you don't know whether that's north, south, east or west.

This is... basically what I'm saying? How will you make a translation without knowing what the words mean in the real world? You can get a vector that says 'oiufd' is to 'flarewq' as 'rye8poq' is to 'nmjrewq'. I don't really think that counts as anything like deciphering the mysteries of Harrappa.

I agree to a very high degree! But I still think that you have greater than zero chance (but not much greater) to make some correct guesses about some very, very basic grammatical forms based on frequencies within other languages. You'd never be able to confirm this, and it's not a very important point.

However, I do strongly disagree with the other point in the comment I was responding to, that you could not tell a real language with meaningful words from one with random words. That (entirely made up and not very helpful) problem does seem like it would be amenable to basis statistical analysis.

I maintain that it is possible. A rock and a rabbit would occur in different contexts. GPT-3 solves this problem in a limited but impressive way, as I mentioned in a comment below just now.

The first question is--if I understand what you're proposing--(relatively) easy. The second is much harder, perhaps impossible.

The reason the first question is relatively easy is because of syntax (sentence order, but also the order of words in phrases). If it's a real language, that order is not random; the English word "the" shows up before "man" much more often than it does before "listens", and so for other words. This is true even in so-called free word order languages, which are really free phrase order languages.

The other thing that could make the first question relatively easy is morphology. If you've decided to make a language that has inflectional morphology (in English, suffixes like -ed and -ing), that also helps determine whether the language is "real", because in a real language not all words will take the same affixes--you can't say 'theseing' (these-ing) in English. In other words, the affixes that you find on a given word are not random.

Of course if you decide not to give your language inflectional morphology, this won't help--but if you do that, then your language's syntax will have to be more rigid: in a language with case marking affixes, like Latin, you don't need word order to tell which noun is subject and which (if any) is object; but you need a more or less fixed word order in English, because (apart from pronouns) we don't have any case marking.

I don't understand how you could say that it ever would. If you don't have anything at all (a related language, partial translations, accompanying illustrations, anything) to compare to, what can you do? You're trying to extract something (meaning) from an object that just does not contain it (text).

Theoretically, given huge amounts of text, you can try to match it with possible human communication, based on the experience of being human - but you would likely need huge amounts of guesses, so it's unlikely to be truly doable with plausible computation resources.

GPT-3 proves that it is possible. It can reason about and answer novel questions just by looking at huge amounts of text.

GPT-3 does not understand language like you and I do with reference to the world. It has no senses, so it is not possible for it to do so. It is also to my knowledge not able to translate anything, so even if we were able to make it "learn" Harrappan writing from these seals, it would not be able to generate English text from them, because the information required to do so does not exist.

To reply to your other comment, rabbit and rocks would occur in different contexts, but you have no way of knowing which is which. You can get numbers, probabilities and mutual information scores and vectors and what have you, but if you can't ground them in anything, you have no way of getting to meaning, and therefore no way of translating.

> It is also to my knowledge not able to translate anything

It translates very well actually. I've tried with English/French and English/German with very good results.

I think it is quite well understood that deciphering a language without external information is possible? But GPT-3 was a recent example that I think proves it. There are probably tons of literature about this question. I think aliens would be able to understand lots of things about Earth just by listening in.

> To reply to your other comment, rabbit and rocks would occur in different contexts, but you have no way of knowing which is which. You can get numbers, probabilities and mutual information scores and vectors and what have you, but if you can't ground them in anything, you have no way of getting to meaning, and therefore no way of translating.

Well, GPT-3 translates very well, so this argument can not be correct. I don't see why it would be like that? It seems quite intuitive that it would be possible to extract meaning.

GPT-3 trained on only English can produce French text? Citation please! I've looked for one, but I can't find it. I can, however, find more than a little information suggesting that GPT-3 is trained on multilingual data.

> I think it is quite well understood that deciphering a language without external information is possible?

I don't think this is understood at all, and I would have to ask you for just one example of it. I would, in fact, say it is categorically impossible, if by "no external information" you mean that we have only text or sound.

The reason that it is not possible to extract meaning is that meaning is a substance that is not present in the text. It is more than the relations between words, in that it is connected to the world.

I am looking more into the exact nature of the multilingual data used by GPT-3 and it's a little hard. The main paper mostly focuses on the algorithms which is less interesting to me.

The key is, if you have aligned or parallel texts, then you can use structural similarities in the lexicon to bootstrap a mapping from one language to another without referring to substantive meaning (which I will maintain is how humans do translation), even if you don't have a dictionary as such. This is not surprising, but I would expect it to have severe limits.

> You can get numbers, probabilities and mutual information scores and vectors and what have you, but if you can't ground them in anything, you have no way of getting to meaning, and therefore no way of translating.

But you have a grounding! You know it's a language used by humans.

deep contemplation combined with the lack of information may lead to completely wrong conclusion. example is medieval interpretation of Egyptian hieroglyphics - completely and laughably wrong, but coherent and making sense, just based on limited amount of inputs.

Presumably your deep contemplation would take the risk of overfitting into account.

Yes, I agree for this case.

I was only elaborating the more general point that in practice merely being faster can let you do new things.

Humans have already spent combined person-centuries trying to crack Linear A, and been able to guess a few words from context. If you use the sound values from Linear B (which can be guessed via Greek), you can even try to pronounce them out loud. Unfortunately, that doesn't help at all unless you can find a related language to help guess more words. If it's a language isolate without living relatives, that would be simply impossible.

However, that doesn't mean that trying to solve it with ML is completely useless. If you can do a century's worth of hypothesis-testing in a fraction of the time to discover that none of them work, at least humans will waste less time trying to solve an unsolvable problem.

Can you give more context for the "wise men in boats" idea?

http://www.chrisjoneswriting.com/terry-pratchett-quotes/ankh...

Pratchett, Terry