A computational linguistic farce in three acts

http://www.earningmyturns.org/2017/06/a-computational-linguistic-farce-in.html

by lx • 9 years ago

101 52 9 years ago

danidiaz• 9 years ago

When he talks about the "computational models of language" that ruled in the 80s, is he referring perhaps to stuff like Montague semantics? https://plato.stanford.edu/entries/montague-semantics/ Or is Montague semantics merely a descriptive framework without practical applications?

What were the main "practical" approaches for natural language understanding back then?

JohnStrange2• 9 years ago

I kind of disagree with some of the premises in the article. I've seen an HPSG for German in the late 90s that was able to parse almost any sentence I could throw at it correctly from a syntactic perspective.

The main problem for natural language understanding is not parsing and not even the semantic and pragmatic representations per se, it has always been the understanding. This requires an adequate knowledge representation and the drawing of inferences from it, and I don't believe that any substantial advances have been made in that field. Computational ontologies have grown larger and there are more "frameworks" than you can count, but none of them offer much knew and promising approaches like geometric meaning theories are in their infancy. Knowledge representation and, generally speaking, the problem of how to integrate different information sources in useful ways are essentially unsolved problems.

Just my 2 cents. Note that I'm talking about the principal problems, not about specific practical applications for which you can use the statistical sledgehammer to some extent.

jesuslop• 9 years ago

Recently Coecke comments on Gärdenfors geometric meaning in the context of his categorical semantics that I'm finding interesting, in arXiv:1608.01402. What I would welcome is a computational link relating that semantics and oldie semantic-network based ideas. For instance in arXiv:1706.00526 description logic based knowledge representation is cast in string diagrammatic, categorical terms, and that at least puts the meaning realm in the same mathy foot.

cjauvin• 9 years ago

Geometric meaning theories.. this sounds like intriguing stuff, could you please point to a decent primer text about it?

JohnStrange2• 9 years ago

References for you and the other poster who asked:

Peter Gärdenfors: Conceptual Spaces - the Geometry of Thought. MIT Press 2000 (Paperback 2004).

It is very easy reading. The problems of geometric meaning theory are compositionality and quantification - how to get the expressivity of logical representations in addition to nearness measures, fuzziness and so on. There are some interesting approaches:

Martha Lewis & Jonathan Lawry: Hierarchical conceptual spaces for concept combination. Artificial Intelligence 237 (2016): 204-227.

Diederik Aerts, Liane Gabora, Sandro Sozzo: Concepts and their dynamics: a quantum-theoretic modeling of human thought. Topics in Cognitive Science 5 (4) (2013):737-772. [and other work by Aerts]

Aerts work is fascinating me personally, but it's unfortunately above my level of mathematical maturity. This is a general problem in this literature, maybe some solutions are already there but they also need to be sold in a way that allows linguists to understand and use the methods. Montague was lucky (well, not personally, of course), because he had scholars who were able to package his dense ideas in more verbose and easier to access textbooks.

Another short book worth reading in my opinion, though very programmatic in nature:

Jens Erik Fenstad: Grammar, Geometry, & Brain. CSLI Publications 2009.

SomeStupidPoint8• 9 years ago

Semantics is syntax.

All semantics has ever been about is not causing parse errors during the decoding step of the sentence, and the constraints imposed on that.

'Syntax' is usually confined to "low level" concerns, while 'semantics' to those above, but the distinction is arbitrary and artificial.

There is no meaning but usage.

hcs• 9 years ago

What do you mean?

throwawuyar32312• 9 years ago

geometric meaning theories

Apologies, I'm an outsider to the field, but what exactly are you referring to here ? The whole vector-space semantic embedding that was popularized by works like word2vec ?

paulsutter2• 9 years ago

This relates to the big Twitter uproar over this blog post:

https://medium.com/@yoav.goldberg/an-adversarial-review-of-a...

And here's the meat of his response:

Idea! Let's go back to toy problems where we can create the test conditions easily, like the rationalists did back then (even if we don't realize we are imitating them). After all, Atari is not real life, but it still demonstrates remarkable RL progress. Let's make the Ataris of natural language!
But now the rationalists converted to empiricism (with the extra enthusiasm of the convert) complain bitterly. Not fair, Atari is not real life!
Of course it is not. But neither is PTB, nor any of the standard empiricist tasks, which try strenuously to imitate wild language

naturalgradient• 9 years ago

Leading somewhat off-topic, but this has also sparked a rather frank debate on r/machinelearning about some of the things discussed in the review, in particular arxiv flag painting:

https://www.reddit.com/r/MachineLearning/comments/6gke6a/d_r...

YeGoblynQueenne9• 9 years ago

My reading is that Pereira doesn't think that deep learning has quite conquered language, and in this he's in complete disagreement both with Goldberg and Le Cunn's side (who both champion deep learning for NLP and claim that it has led to great advances in the field).

For me the problem with NLP and deep learning, or indeeed any empirical method, is that the evaluation metrics we have are imperfect. Take BLEU scores, from Goldberg's post, for instance. Those basically compare generated text to some arbitrary target. Originally, they were proposed as metrics of machine translation quality, so the target was some existing translation and the machine-generated translation was examined for coverage of this human-made translation. But of course, there is no principled way that we know of to choose one translation over another- or even say whether a translation is a good or bad translation, on its own. And that's true for translations by humans also. You give the same text to 10 professional translators, they'll give you 10 different translations. Then you give each of their translations to 10 readers and ask them for their opinion, and you get back 100 different opinions.

The translation task itself is not even particularly well defined, exactly because there may be any number of valid translations (possibly, infinitely many) of a piece of text in another language. So, with translation, we have an ill-defined task with an arbitrary metric. And that metric of course is lifted from its original task and used to evaluate language generation and so on. Then someone comes along who knows how to train a deep net but has no idea what the purpose of their chosen metric is, or what it does and has no understanding of the task itself- and claims to have solved it because they got good results on that metric.

It's a bit of a methodological mess that's not going to lead to much progress. People can keep piling on these "results" for as long as they like and pretend that they're "solving" this or that problem- but in real-world terms, nothing is really being solved at all.

empath75• 9 years ago

But deep learning networks are being used in production every day aren't they?

paulsutter2• 9 years ago

Google translate is now based on a neural network and you can be sure they have solid metrics. By analogy Google search has a large panel of humans whose subjective feedback is used to test the quality of search algorithm variations.

unhammer• 9 years ago

Bit of an aside: Apparantly ChrF – character-level n-gram f-score – is the new hotness in evaluating MT systems http://www.aclweb.org/anthology/W/W15/W15-30.pdf#page=412

throwawuyar32312• 9 years ago

I have to wonder if English is really the best language for NLP research. Things like the Winograd schemas which have attracted a lot of attention simply aren't possibilities in other languages.

Why not start working with more structured agglutinative * languages like Japanese/Korean and Indic family (Sanskrit esp.) .

How about other European languages ? Are they better structured empirically ? I hear German is very grammatical, and that Hungarian is ... erm odd ?

( Note: I know occidental tradition likes to split Indic tongues, and Indo in Indo-European is not considered agglutinative. I don't subscribe to this view. I use agglutinative in the sense of Panini: "particles" sticking to stems/roots/words - phonetic modifications are irrelevant for grammar.)

vedant• 9 years ago

One reason is that the amount of training data is many many orders of magnitude smaller.

FWIW it seems the structure you're talking about exploiting is at a morphological and syntactic level, which modern language models tend to effectively handle. Semantics are a much harder problem.

lgessler• 9 years ago

I hear German is very grammatical, and that Hungarian is ... erm odd ?

Just want to point out that "grammatical" probably isn't the word you want here. Every language is grammatical by definition in the sense that there are rules that govern its sound system, word formation system, syntax, etc.

The concept you're getting at, though--that some languages are easier for computer programs and/or speakers of Indo-European languages to understand--is sound.

mark_edward2• 9 years ago

do you think analytic would be a good term here? i heard mandarin is very analytic language, maybe that could be a good choice

mchaver• 9 years ago

Things like the Winograd schemas which have attracted a lot of attention simply aren't possibilities in other languages.

I do not think that is correct. Anaphora exists in many languages. Check out the Anaphora article on wikipedia and click on different language versions. There are example sentences for many languages.

https://en.wikipedia.org/wiki/Anaphora_(linguistics)

There are translation for the Winograd Schemas into a couple of languages. Granted I found some of the translations a little unnatural in some cases but they are still understandable and expose the problem.

http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/...

http://arakilab.media.eng.hokudai.ac.jp/~kabura/collection_j...

http://www.llf.cnrs.fr/winograd-fr

throwaway-12092• 9 years ago

The whole field of NLP and computational linguistics reminds me of that joke where a drunk is looking for his keys under a street lamp instead of where he actually lost them.

This is true in particular of anything that pertains to reasoning and knowledge representation. People still are trying to "infer rules" and do logical, rather than probabilistic reasoning. I get why that is. To me though, the kind of real life reasoning that humans do seems heavily probabilistic and contextual, Bayesian almost. And there's next to no notable work going on in that direction.

johnbender2• 9 years ago

I don't think these two things are mutually exclusive.

As far as I'm aware there is work underway to take logical constructions and integrate them with probablistic machine learning to do things like force zero probabilities in impossible input cases. That is encoding domain knowledge into the model directly in the form of symbolic reasoning.

I mean even Bayesian nets require some encoding of causality right? Maybe I'm reading to much of "blah symbolic reasoning is worthless" in your comment?

throwaway-12092• 9 years ago

It's not worthless, per se, it's just not a precursor to AGI in any shape or form, no matter how much the researchers pretend otherwise.

YeGoblynQueenne9• 9 years ago

> People still are trying to "infer rules" and do logical, rather than probabilistic reasoning. I get why that is.

That is because it's very hard to collect statistics on something that you can't really quantify- meaning, in this case.

There was a thread on HN a couple of days ago about a blog post where someone was experimenting with, among other things, training an LSTM network to generate Java programs [1]. In one example, the LSTM did really well in reproducing the structure of a Java program, with import declarations, followed by a class implementing an interface with a few methods with structured comments and throws declarations and everything- and even a test!

On the other hand, this program was completely useless. From a cursory glance it would probably not even compile (e.g. it refered to undeclared variables etc). There was one method named "numericalMean()" that took a single double and returned an (undeclared) variable "sum". The class had a nonsensical name - "SinoutionIntegrator". The test was testing something called "Cosise", presumably a method- but not one defined in the class. In short- a mess.

That might sound a bit harsh, but I think it's a very good example of why statistical NLP is really bad at doing meaning: because there is nothing, not a shred, of meaning in examples of the data we use to train statistical models of language, i.e. text.

Because, you see, the relation between meaning and text (and even spoken language) is completely arbitrary. Or, to put it in another way, there are potentially an infinite number of valid mappings between structure and meaning, of which we, human beings, somehow by convention or some other crazy mechanism, have agreed to use just one. And even though the various forms language entities take (inflections etc) are used exactly to convey meaning, right, the rules of how meaning varies with structure are, again, completely independent from structure itself.

Now, we have done very well in modelling structure, from examples of it (which is what text is). But it's completely unreasonable to expect our algorithms to be able to extract meaning from it also.

And that is why people are still trying to put down the rules of meaning by hand. Because that's the only way we can think of, currently, to process meaning automatically.

________

[1] https://news.ycombinator.com/item?id=14526305

YeGoblynQueenne9• 9 years ago

So, I understand this blog post is about something else completely (the internet argument started by Yoav Goldberg on Medium, reportedly) but for me the really interesting part is the historical information in it. I wish Fernando Pereira could find the time to expound a bit on all those parenthetical notes in his blog post, perhaps even write a short book on the history of AI.

AI is kind of a strange beast like that: it's gone through a few very different phases and it's difficult for one person to understand all of them equally well. Which of course makes it even harder to avoid reinventing wheels and repeating mistakes. A bit of history would do us all a world of good.

Btw, I'm getting the feeling most people here will probably hear of Fernando Pereira for the fist time but he has a very long career in AI and NLP. He was a prominent symbolicist, with some important contributions to logic programming (he was one of the co-founders of Quintus, the company that sold the first commercial Prolog, along with Warren, Byrd and others). Then he turned to statistical AI and now he's a VP at Google (a.k.a. the den of the connectionists, if I may be so bold). He's probably one of the few computer scientists around who understands both symbolic and statistical AI in equal measures. If anyone is qualified to talk about their relative merits, that's him.

(and if I sound like a bit of a fangirl- that is because I basically am. Pereira is one of my logic programming heroes and a great teacher to me, albeit unbeknownst to him :)

wodenokoto• 9 years ago

What is the Atari referred to here?

Does it have somethi g to do with the game playing AI from openAI? And if so, how is that even related to NLP?

A computational linguistic farce in three acts

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