ACL 2011 Tutorials

Here are some notes from the ACL tutorials:

Formal and Empirical Grammatical Inference
Jeffrey Heinz, Colin de la Higuera and Menno van Zaanen

Jeffrey and Colin motivated and presented the main results for formal grammatical inference. Even though many theoretical results are negative, learning is usually possible by restricting the model class (to a well defined subset used by natural languages) or assuming a non-distribution-free setting. Colin's book was recommended as a good introduction to the theory. It would be interesting to see if Turkish morphotactics or morphophonemics fall into one of the easy-to-learn model subclasses. You can download the slides.

Rich Prior Knowledge in Learning for Natural Language Processing
Gregory Druck, Kuzman Ganchev, Joao Graca

There is a recent trend for encoding prior knowledge in learning problems not in the prior distributions but later in the learning process. The prior knowledge usually comes in the form of feature-class expectations and guiding the model toward the correct expectations is only possible after considering the input. Posterior regularization, constraint driven learning, and generalized expectation criteria seem to be related implementations of this idea. You can download the slides.

Dual Decomposition for Natural Language Processing
Michael Collins and Alexander M Rush

I did not attend this one but here are the slides.

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Semantic Structures by Ray Jackendoff

Jackendoff divides the study of the language faculty into three components: phonological, syntactic, and conceptual. In speech recognition a model that tells us what word sequences are likely is essential to recognition accuracy, because the acoustic information is often ambiguous (e.g. "She kissed this guy." vs. "She kissed the sky."). Stated in Jackendoff's terms, a probabilistic model of the syntactic component is helpful in disambiguating what is going on in the phonological component. Similarly I think a model of what is likely in the conceptual component is essential to resolving ambiguities in the syntactic component. If "what is likely to be said" is essential in interpreting "what is heard", then "what is likely to be meant" is similarly essential in interpreting "what is said".

Unfortunately we do not have good conceptual models yet, so computational linguists still try to make do with error prone hand tagging and shallow machine learning to disambiguate senses, references, and relations.

On a side note, each component in Jackendoff's work is modeled after the generative paradigm which, for the syntactic component, is described as follows:

1. Speakers can understand and create an indefinite number of sentences they have never heard before.
   
2. Therefore the repertoire of syntactic structures cannot be characterized as a finite list of sentences.
   
3. Nor can it be characterized as an infinite list of possible sentences because we have finite brains.
   
4. Thus it MUST be mentally encoded in terms of a finite set of primitives and a finite set of principles of combination that collectively generate the class of possible sentences.

Am I the only one befuddled by this argument? Primitives plus means of combination is certainly one way to create infinity using finite means, but why assume it is the only way? Dynamic systems, random processes, who knows what else can lead to infinite possible outcomes from a finite initial endowment. Why just present two strawmen, finite and infinite lists, as the only alternatives to discrete primitives and combination? Why after a couple of paragraphs further narrow the description to "the argument from creativity to the NECESSITY for principles or rules in syntactic knowledge"? Discrete primitives with finite and definite constraints and rules of combination is one way to build a representational system, unlikely to be the correct way for all three components of language, and certainly not the only way.

See also: Plausibility vs. Inference.

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The Noisy Channel Model for Unsupervised Word Sense Disambiguation

Seminar presentation at ITU on our CL paper.
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A tutorial on language modeling with SRILM

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From Eternity to Here by Sean Carroll

Why is the future different than the past? The fundamental rules of physics seem as symmetric in time as they are for different directions of space, yet you can't unscramble an egg and you can't remember the future. The answer seems to lie in the concept of entropy, which tends to increase over time in a closed system unlike energy and momentum which stay constant. Yet, entropy is one of these concepts that lead to quite a bit of confusion, sort of like (and sometimes for the same reasons as) probability. Sean Carrol's wonderful book tackles the mysteries of entropy and arrow of time. Here are some entropy puzzles and some further reading:

Entropy puzzles:

• If you find yourself in a universe with low entropy it is as likely to have come from a higher entropy state as it is to evolve into a higher entropy state. So how do you know you are not a Boltzmann brain?
  
• Take a box with a partition in it, with gas A on one side, gas B on the other side, and both gases are at the same temperature and pressure. Remove the partition. If gas A and B are different gases, there is an entropy that arises due to the mixing. If the gases are the same, no additional entropy is calculated. What if you thought they were the same gas and years later it was discovered that they happened to be two different isotopes? (See Gibbs paradox and E.T. Jaynes' paper). More generally this microstate / macrostate business seems completely user defined and arbitrary, so how can it have real physical effects?
  
• In a reversible system there must be just as many paths that decrease the entropy as that increase the entropy. Why don't we observe as many of the first type as the second?
  
• A gas squeezed in the corner of a room will tend to spread thereby increase its disorder and entropy. If we add an attractive force like gravity matter seems to clump together rather than spread out. How does clumping together increase entropy?
  
• A rotting plant turns into dust and gas which increases disorder and entropy. A seed turns a bunch of gas and dust into a full grown tree which seems to decrease entropy. This can only happen because the seed is not a closed system and is using the energy from the sun and ends up increasing the overall entropy of the universe at the end. When, how, and why does this type of thing happen?
  

Further reading:

• From Eternity to Here: Sean Carroll's book that inspired this post.
  
• Labyrinth of Time: Michael Lockwood's book on the arrow of time with a bit more philosophy and a bit less black hole physics.
  
• Time and Chance: David Z. Albert's book gives one of the clearest discussions of thermodynamics (both classical and statistical) and its relation to the arrow of time.
  
• Good and Real: Gary L. Drescher's book that tackles not only the arrow of time, but quantum indeterminacy, consciousness, free will, and ethics. This is one of my favorite books which inspired a few earlier posts.
  
• E. T. Jaynes has a number of articles that clarify some of the mysteries. See also these links.
  
• Permutation City: One of my favorite sci-fi novels by Greg Egan explores the nature of time, simulation and reality. Maybe instants are not ordered in time at all, the state of our short term memory seems to give this impression.
  
• Max Tegmark at MIT came up with the Mathematical universe hypothesis which is reminiscent of the Dust theory that underlies Permutation City. See also Hans Moravec's essay.
  

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