• ComputationalAccountsOfProduction

Computational Accounts of Production

Synopsis:

• Connectionist and spreading-activation models of language production (lexical and syntactic production, but with a focus on speech errors)
• Information theoretic models of incremental language production (phonetic, morphological, syntactic, and extra-syntactic preferences)
• Computational models of adaptation in language processing (implicit learning, ideal observer models)

We will start with a quick refresher (written for language researchers) on probability theory and information theory and then read a lot of papers on examples of how information content, entropy, etc. affect language production. The goal of the class would be to provide a thorough introduction to these topics, but also to discuss the short-comings of these types of accounts and their relation to other mechanistic accounts of language production.

Prerequisites

The seminar is intended for graduate students though I may consider advanced undergraduate students with a psycholinguistics background and strong interest. A very basic background in probability theory is assumed, but we'll go through the basics at the beginning of the class.

Requirements

This will be a reading/discussion seminar (not a lecture). So, even if you plan to audit I would appreciate if you do the readings (see webpage for more detail on requirements etc.).

Students who are taking the class for credits will have to prepare for every discussion. I plan to use the BlackBoard forum feature and students taking the class for credit will have to post 2 questions or comments at least 1 day before each class about the readings. Additionally, they will have to lead some discussions. There also will be a final project, which can be a discussion paper or a proposal for an experiment (or grant ;). The final write-up should be about 4-10pp.

Readings

There will be a lot readings for each day, but the goal is not for all of them to be read by everyone. Instead, we will have a short obligatory reading and then distribute additional readings across people in the class. Discussion leaders have to have read all of the papers.

Syllabus

This is a very rough draft of a syllabus. I am also blatantly stealing parts of a great class taught by Dan Jurafsky and Michael Ramscar at Stanford (Fall 2009). The list below is meant as a superset suggestion (covering all topics would take more than a semester). Please feel free to suggest additional topics or to tell me your favorites.

1. Class: Overview and Early approaches to efficiency: The Principle of Least Effort
   • Zipf 1949 (1-22) and Zipf 1935 (20-39, 172-176) on the inverse frequency-form link [attachment:Zipf35-49.pdf pdf]
     • Zipf, G.K. 1935. The psycho-biology of language: An introduction to dynamic philology. Houghton Mifflin.
     • Zipf, G.K. (1949). Human behaviour and the principle of least effort: An introduction to human ecology. New York: Hafner.
2. Class: Zipf continued, early evidence from phonology and speech
   • Zipf 1935 (73-81, 109-121) and Zipf 1949 (98-108) on phonological change [attachment:Zipf35-49_sound.pdf pdf]
   • Zipf 1935 (283-287) on speech rate (velocity of speech) in same file
   • also covered:
     • Schuchardt, H. 1885. On sound laws: Against Neogrammarians. Translated by T. Vennemann and T.H. Wilbur.
3. Class: Basics of Probability Theory and Information Theory, as well as early applications to language
   • Background reading:
     • John A. Goldsmith. 2007. Probability for linguists. [attachment:Goldsmith07.pdf pdf]
     • Sheldon Ross. 2010. A First Course in Probability. Eigth Edition. Section 9.3 "Surprise, Uncertainty, and Entropy", pages 425-429. [attachment:Ross10.pdf pdf]
   • Shannon, C. Prediction and entropy of printed English. Bell System Technical Journal, 30, 50-64. [attachment:shannon51.pdf pdf]
   • Thomas M. Cover and Roger C. King. 1978. A Convergent Gambling Estimate of the Entropy of English. IEEE Transactions on Information Theory 24:4, 413-421. [attachment:coverking78.pdf pdf]
4. Class
   • Manin, D. 2006. Experiments on predictability of word in context and information rate in natural language.

Topics

Computational Approaches to Production

1. Background in probability theory and information theory

   • Robert A. Rescorla. 1988. Pavlovian Conditioning: It's Not What You Think It Is. American Psychologist, 43(3), 151-160 PLUS

   • 2. Early applications of information theory to natural language: The entropy of English

   • Shannon, C. Prediction and entropy of printed English. Bell System Technical Journal, 30, 50-64.
   • Thomas M. Cover and Roger C. King. 1978. A Convergent Gambling Estimate of the Entropy of English. IEEE Transactions on Information Theory 24:4, 413-421.

2. Least Effort

   • Zipf (1929/49)
   • Manin (2006, 2007)

3. Shannon Information and Sub-Phonemic/Phonemic Reduction

   • Duration reduction (Bell et al. 03, 09); Aylett and Turk 04; Pluyymaerkers et al. 05)
   • Vowel weakening (Van Son and Van Santen, 05)

4. Shannon Information and Sub-Phonemic/Phonemic Reduction

   • Phone deletion (Cohen Priva, 08)
   • Fluency (Shriberg and Stolcke 96)

5. Shannon Information and Morpho-syntactic Reduction

   • Auxiliary reduction and omission (Frank and Jaeger 08)
   • Prefix deletion (Norcliffe and Jaeger 10)
   • Case-marker omission

6. Connectionist Models of Lexical Production

   • Speech errors (Dell, 86)

7. Connectionist Models of Syntactic Production

   • Chang et al

8. Shannon Information and Syntactic Reduction

   • Wasow et al 07; Jaeger 10a,b

9. Relative Entropy and Argument Omission

   • Argument drop (Resnik 96)
   • Ellipsis

10. Uncertainty Reduction and Referring Expressions

    • Wasow, Perfors, and Beaver
    • Tily and Piantadosi

11. Shannon Information and Neighborhood Entropy across the Discourse

    • Genzel and Charniak (2002, 2003)
    • Piantadosi and Gibson (2008)
    • Qian and Jaeger (2009, 2010a,b)

12. Optimal Lexica

    • Information density, Neighborhood density, Ambiguity (Piantadosi et al 09; Plotkin and Nowak; Gassner 04)
    • Phonological optimality (Graff and Jaeger 09)

13. Information theoretic approaches to Morphological Paradigms

    • Baayen
    • Moscovo del Prado Martin

Computational Models of Priming, Implicit Learning, Adaptation

1. Priming and Implicit Learning

2. Computational Models of Skill Maintenance

   • Huber et al

3. Connectionist Models of Syntactic Priming

   • Chang et al

4. ACT-R Models of Syntactic Priming

5. Surprisal and Surprisal-based Models of Syntactic Priming

   • Hale 01; Levy 08

   • Snider & Jaeger

6. Phonetic Adaptation

   • Clayards et al 09; Kraljic and Samuel

7. Syntactic Adaptation

   • Wells et al 09; Sauerland et al., 09

8. Ideal Observer Approaches to Adaptation