• 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 and 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 ;).

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.

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
   • For those with no probability theory or information theory, start with: John A. Goldsmith. 2007. Probability for linguists.
   • For those with no information theory, the above plus: Sheldon Ross. 2010. A First Course in Probability. Eigth Edition. Section 9.3 "Surprise, Uncertainty, and Entropy", pages 425-429.

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.

3. Least Effort

   • Zipf

4. 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)

5. Shannon Information and Sub-Phonemic/Phonemic Reduction

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

6. Shannon Information and Morpho-syntactic Reduction

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

7. Connectionist Models of Lexical Production

   • Speech errors (Dell, 86)

8. Connectionist Models of Syntactic Production

   • Chang et al

9. Shannon Information and Syntactic Reduction

   • Wasow et al 07; Jaeger 10a,b

10. Relative Entropy and Argument Omission

    • Argument drop (Resnik 96)
    • Ellipsis

11. Uncertainty Reduction and Referring Expressions

    • Beaver et al
    • Tily and Piantadosi

12. Shannon Information and Neighborhood Entropy across the Discourse

13. Optimal Lexica

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

14. 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