Knowledge of Grammar, Knowledge of Usage

1. Knowledge of Grammar, Knowledge of Usage Syntactic Probabilities Affect Pronunciation Variation (Gahl and Garnsey)

2. Topics • The Dialogue • Experimental design • Results and Discussion

3. The Dialogue • Papers arguing that grammar is probabilistic • Bybee 2001, Jurafsky 2001, 2003a, Henry 2002 • Example claim: “the way language is used affects the way it is represented cognitively” • This is an obvious critique of UG • But - Newmeyer 2003 “Grammar is grammar and usage is usage”

4. Newmeyer 2003 abstract • “A number of disparate approaches to language, ranging from cognitive linguistics to stochastic implementations of optimality theory, have challenged the classical distinction between knowledge of language & use of language. Supporters of such approaches point to... language users' sensitivity to the frequency of occurrence of grammatical elements.... In this article I defend the classical position & provide evidence.... The article also questions the relevance of most corpus-based frequency & probability studies to models of individual grammatical competence.”

5. Some particular criticisms • Probabilities are a function of meaning,not syntax; i.e. one interpretation is much more likely in the world, but this shows nothing about Grammar • Much previous research could be seen as looking at probabilities of word adjacency, but grammar is about syntactic categories (verb, noun). • The current paper is Gahl and Garnsey’s response

6. G&G Lit Review • This is the literature review, not their own research! • Frequent words tend to be shorter. High-frequency multi-word expressions tend to shorten. • Repeated words and words presented earlier in discourse tend to shorten. G&G note this makes no reference to syntax.

7. Lit Review 2 • Reduction can be overall shortening or segment deletion. • Previous work shows that lexical frequency is a significant predictor of /t,d/ segment deletion (just, fast, went) • Certain word-to-word associations cause shortening (Supreme Court)

8. Lit review 3 • Bybee 2002 showed that final segments of words that tend to appear before consonants, where deletion or lenition is likely, are more prone to delete word-finally. – This is a phonological category frequency effect. • G&G – so maybe other levels of abstraction, i.e. syntax, would be similar? • Active verbs more prone to segment deletion than passive verbs

9. Criteria for Experiment Design • Probabilities must be based on phrase types rather than specific lexical items (“sexy” and “Hunter” while highly frequent together are not phrase types) • They should make reference to explicitly syntactic relationships (that a generativist would accept like subcategorization and not simple adjacency) • Not based on plausibility or meaning

10. Verb Bias does the Trick • Verb bias is the probability that a verb will appear in a particular syntactic structure • Some verbs biased towards appearing with a direct object. – We confirmed the date of our visit. • Some verbs biased towards appearing with sentential complements – We believed Hunter makes the best presentations always. • Bias is determined based on corpus and norming studies.

11. Bias-conforming and Bias-Violating • Sentences in which a DO-Biased Verb actually has a DO are bias-conforming • Sentence in which a DO-Biased Verb occurs with an SC are bias-violating. • Previous studies show that bias-conforming sentences are processed more rapidly and with greater accuracy than bias-violating ones.

12. Verb-bias meets all 3 criteria • Verb bias applies not to single words but to all verb complements of a given type (DO or SC) • Verb bias is about a syntactic relationship – the sort of complement taken – not simple adjacency or co-occurrence • The difference is not based on meaning. SC and DO complements can be equally plausible, and yet the verb is still DO-biased.

13. Verb Bias and Phonetics • G&G will use verb bias and phonetic modifications to study probability in syntactic representations • Focus on the verb itself and the NP following the verb (and pauses). The NP following the verb will be a Subject if the verb complement is a clause and a DO if it is not a clause.

14. Hypothesis 1 • Verbs in bias-matching contexts will be more likely to undergo /t,d/ deletion than verbs in bias-violating contexts • So a DO-biased verb will delete in a DO context, and an SC-biased verb will delete in an SC context • Motivated by previous studies showing high lexical frequency and high word-to-word probability cause deletion

15. Hypothesis 2 • Bias-violating prosodic boundaries will affect the duration of words and pauses greater than bias-matching boundaries • DO-verbs will be longer in SC contexts than when in DO contexts • SC-verbs will be longer in DO contexts than in SC contexts. • Clauses have prosodic boundaries; this will be compensated for in bias-matching contexts by the reductions, but there will be no such compensation in bias-violating ones, making them longer

16. Table 1: Examples • The CIA director confirmed (Short) the rumor once it had spread widely. • The CIA director confirmed the rumor (Long) should have been stopped sooner. • The job applicant believed the interviewer (Long) when she discussed things with her. • The job applicant believed (Short) the interviewer had been dishonest with her.

17. Methods • 10 verbs of each bias type • Divided up into groups and randomized • 20 students (10 M, 10 F) • Each verb appeared once in bias-conforming context and once in bias-violating context • Participant were asked to read the sentences first and then when comfortable with them, read them aloud. • Controls for equal plausibility from earlier norming and corpus studies. • Used Praat to measure durations.

18. Results • Everything they thought was right and everything those silly UG people thought was wrong

19. The End • The End • Thank you

20. Results 2 • /t,d/ deletion rates were higher in bias-conforming contexts than in bias-violating ones. (Figure 1) • The rates did not deviate based on verb-type (SC vs DO verbs) but on whether or not the bias matched the context. This is critical.

21. Logistic Regression • Logistic regression is a statistical model that relates one or more predictors and looks for significance. • Eight possible predictors (G&G: 760-1), some were significant, some not. • Importantly, the match between verb bias and syntactic structure was one of them. • They are not arguing that Bias-matching is the only predictor of all phonetic reduction, just one.

22. Strength of Bias • This is big – the strength of the bias predicted the likelihood of deletion. • So a strongly biased DO-verb was much more likely to cause deletion than a weakly biased SC-verb.

23. Further results – Hypothesis 2 • Verbs, post verbal pauses, and following NPs all followed hypothesis 2’s predictions. Duration was longer in bias-violating conditions. • The duration of everything after the ambiguous NP, however, was not predicted by bias-violating. • Repetition had no effect on durations.

24. Discussion – Articulatory Practice • Could the shortening be due to articulatory practice instead of probabilistic grammar? • Unlikely 1 – some of the sentences were high probability and yet might not have ever been encountered before. Remember they are testing grammatical-probability, not lexical. • Unlikely 2 – Their repetition data showed no repetition effects on the verbs.

25. Discussion - Retrieval • Could the lengthening be due to difficulties in retrieval? • Unlikely 1 – Retrieval lengthening is typically associated with overall speaking rate. These results showed no duration changes after the ambiguous NP. Duration modifications were specific. • Unlikely 2 – The participants were asked to read the sentences first before recording, so all lexical items were already primed.

26. Discussion – Speaker Control • Speaker-controlled variation – Speakers choose to exert more or less articulatory effort taking into account factors that might affect intelligibility. • So highly-predictable forms are produced with less articulatory effort than low-probability forms. • G&G think this matches their data

27. Conclusion 1 • This experiment overcame previous objections about meaning and syntactic classes and provided evidence that grammar is probabilistic. (Remember that the strength of bias correlated with the frequency of deletion. It is stochastic.)

28. Conclusion 2 • What is the representation of these probabilistic form variations? Their experiment does not show. • They argue that the simplest theory is to have the grammar itself be probabilistic. • Results are consistent with the idea that grammatical competence is dependent upon language exposure; i.e. not genetics.