A computational linguistic farce in three acts

>> 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.

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

That we artificially decompose the process of accepting a sentence as, eg, proper English into two phases: syntactic correctness and semantic correctness.

However, that distinction is arbitrary -- there is only the question of if the sentence is accepted by an agent (eg, person) as a well-formed sentence.

Any full accounting of the class of well-formed sentences must embed the semantic concerns; violating semantics is a syntax error (albeit, not usually a "first order" one). Similarly, even base syntax, such as the subject/verb/object distinction and ordering is carrying semantic information about word usage. The distinction between the two is non-existent: a full accounting of either must embed the other.

So semantics is syntax -- if you write a system of rules that only accepts valid sentences, then the rules will end up carrying the semantic structure of the language in them.

Ed:

I suppose I left out why this might matter --

In the quest to build an AI that understands semantics (ie, that "understands meaning"), we can bypass attacking that problem directly by training it (eg, a NN) on the full acceptance task (joined syntax and semantics -- classifying a sentence as proper English or not), and then truncate the network away from "low level" features (and perhaps at the other side, focused on 'yes' or 'no') to extract a network that has (most of) the (abstract) semantic structure embedded. We could then utilize these "middle level" features as a sort of rosetta stone, to train low level networks to embed content for them to understand, and high level networks to utilize their output on decisions to repurpose "understanding" across tasks.

I would argue that using things like Word2Vec (or the resulting vector space of words) is a similar idea.

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.

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