• 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 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: Basics of Probability Theory and Information Theory, as well as early applications to language
   • Background reading:

     • John A. Goldsmith. 2007. Probability for linguists. Goldsmith07.pdf pdf

     • Sheldon Ross. 2010. A First Course in Probability. Eigth Edition. Section 9.3 "Surprise, Uncertainty, and Entropy", pages 425-429. Ross10.pdf pdf, see also [http://onlinestatbook.com/]

3. Class: The Entropy Rate of English

   • Shannon, C.E. Prediction and entropy of printed English. Bell System Technical Journal, 30, 50-64. 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. coverking78.pdf pdf

   • Manin, D. 2006. Experiments on predictability of word in context and information rate in natural language. manin06.pdf pdf

4. Class: Constant Entropy Rate Across Discourses

   • Genzel, D. and Charniak, E. (2002). Entropy rate constancy in text. In Proceedings of ACL-02. genzelcharniak02.pdf pdf

   • Qian, T. and Jaeger, T.F. (submitted). Entropy profiles in Language: A cross-linguistic investigation. Entropy. qianjaeger10.pdf pdf

   • also covered:

     • Keller, F. (2004). The entropy rate principle as a predictor of processing effort: An evaluation against eye-tracking data. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Barcelona, pp. 317-324. keller04.pdf pdf

     • Genzel, D. and Charniak, E. (2003). Variation of Entropy and Parse Trees of Sentences as a Function of the Sentence Number. Proceedings of the 2003 Conference on Emprical Methods in Natural Language Processing, pp. 65-72.

     • Qian, T. and Jaeger, T.F. (2009). Evidence for Efficient Language Production in Chinese. In CogSci Proceedings. qianjaeger09.pdf pdf

     • Qian. T. (2009). Efficiency of Language Production in Native and Non-native Speakers
5. Class: the Noisy Channel Theorem

   • Shannon, C.E. (1948). A Mathematical Theory of Communication. Mobile Computing and Communications Review, Volume 5, Number I. Reprinted from Bell System Technical Journal with corrections. shannon48.pdf pdf

6. Class: Zipf continued, early evidence from phonology and speech

   • Zipf 1935 (73-81, 109-121) and Zipf 1949 (98-108) on phonological change Zipf35-49_sound.pdf pdf

   • also covered:

     • Schuchardt, H. (1885) On sound laws: Against Neogrammarians. Translated by T. Vennemann and T.H. Wilbur. schuchardt1885.pdf pdf

7. Class: Functionalist Theories of Language Change
   • Bybee, J.

   • Bates, E. and MacWhinney, B. (1982)

8. Class: Frequency, Predictability and Word Duration

   • Zipf 1935 (283-287) on speech rate (velocity of speech) Zipf35-49_sound.pdf pdf

   • Pluymaekers, M., Ernestus, M., and Baayen, R. (2005). Lexical frequency and acoustic reduction in spoken Dutch. The Journal of the Acoustical Society of America, 118, 25-61. pluymaekersetal05.pdf pdf

   • Alan Bell, Jason Brenier, Michelle Gregory, Cynthia Girand, and Dan Jurafsky. (2009) Predictability Effects on Durations of Content and Function Words in Conversational English. Journal of Memory and Language 60:1, 92-111. belletal09.pdf pdf

   • Also covered:

     • Aylett, M. and Turk, A. (2004). The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and Speech, 47(1), 31-56. aylettturk04.pdf pdf

9. Class Word predictability and Word Pronunciation

   • Van Son, R., and Pols, L. (2003). How efficient is speech? Proceedings of the Institute of Phonetic Sciences, 25, 171-184. vansonpols03.pdf pdf

   • Aylett, M.P. and Turk, A. (2006) Language redundancy predicts syllabic duration and the spectral characteristics of vocalic syllable nuclei. The Journal of the Acoustical Society of America 119, 30-48. aylettturk06.pdf pdf

   • Also covered:

     • van Son, R. and van Santen, J. (2005) Duration and spectral balance of intervocalic consonants: A case for efficient communication. Speech Communication 47(1), 100-123. vansonvansanten05.pdf pdf

Topics

Computational Approaches to Production

1. Background in probability theory and information theory

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

3. Least Effort

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

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

    • Wasow, Perfors, and Beaver
    • Tily and Piantadosi

12. Shannon Information and Neighborhood Entropy across the Discourse

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

13. Optimal Lexica

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

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