SERIAL VS. PARALLEL PHONOLOGICAL GRAMMARS: Learners...

SERIAL VS. PARALLEL

PHONOLOGICAL GRAMMARS: Learners, Errors and

Consequences

Anne-Michelle Tessier University of Alberta Feb 8 2013

University of Toronto

Q: Do phonological processes apply in serial or in parallel?

A selective continuum of answers across theories: • SPE rules: very serial • Lexical Phonology, mostly serial Prosodic phonology rules: • Classic OT constraints: very parallel • Stratal OT: mostly parallel

• Harmonic Serialist constraints: very serial!

Serial vs. parallel processes, from the constraint-based perspective

The Classic OT view: everything at once, all in parallel With great power comes great problems - global interactions: predicting unattested languages - no intermediate forms: failing to predict opacities, etc.

(summaries: McCarthy 2007, McCarthy 2011)

/fonɑlədʒi/ Max-C Max- V *Unstressed

Full-V

Ident-VPlace

fo(ˈnɑ.lə)(ˌdʒi) *!

fə (ˈnɑ.lə)(ˌdʒi) *

(ˈfnɑɫ)(ˌdʒi) *!*

(ˈsɪn.tæks) *!*** *!***

Serial vs. parallel processes, from the constraint-based perspective

The Harmomic Serialist (HS) view: one ordered thing at a time, slow and steady harmonic improvement

Upshot: - a finite candidate set of small input-based changes - for what benefits? at what costs?

/fo.ˈnɑ.lə.ˌdʒi/ Max-C Max- V *Unstressed

Full-V

Ident-

VPlace

fo.ˈnɑ.lə.ˌdʒi *!

fə.ˈnɑ.lə.ˌdʒi *

ˈfnɑ.lə.ˌdʒi *!

(ˈsɪn.tæks) not in the candidate set!

Serial vs. parallel constraint interactions from the learning perspective

Successes in OT learning • formal algorithms that approximate child development

(Tessier 2007, 2009, 2010; Jesney &Tessier 2008, 2011; Becker & Tessier 2011)

• what’s easy: phonotactics

observed: [dɑgz] [kæts] voicing assimilation • what’s harder: alternations

observed: [dɑgz], [kæts], [fɪʃəz] ‘plural’ UR = /-z/

Serial vs. parallel constraint interactions from the learning perspective

Successes in OT learning • formal algorithms that approximate child development

(Tessier 2007, 2009, 2010; Jesney &Tessier 2008, 2011; Becker & Tessier 2011)

• what’s easy: phonotactics

observed: [dɑgz] [kæts] voicing assimilation • what’s harder: alternations

observed: [dɑgz], [kæts], [fɪʃəz] ‘plural’ UR = /-z/

HS as a theory of learning? • Will the old tools work? • What gets harder? Easier? Different? • Does typology improve? • Does the learner trajectory match human learning?

Roadmap of the talk

1. A Biased Introduction to Error-driven OT & HS learning

2. One Advantage of Serial Learning: Avoiding Backtracking - A gradual OT (but not HS) learner makes odd errors - Diary study evidence: such errors not child-typical

3. One Challenge for Serial Learning: Acquiring Inventories - HS needs more, hidden, rankings than OT - Hidden rankings are hard to learn

4. Interim Conclusions and Take Home Messages - Learning crux: HS shrinks the candidate space - Maybe this is a good thing?

Tessier (2009); building on Prince and Tesar (2004), Hayes (2004) interalia

Learners hypothesize one grammar at a time

but store all previous forms and errors

Learners are triggered to change grammars by the accumulation of error types

See also Becker and Tessier (2011) about variation in production

1a. An Error-Driven OT Learner

A Categorical Grammar

A Grammar

Grammar at Work

Grammar’s Results

/sɑk/ *Fric *Coda Ident[cont] Max

sɑk *! *!

sɑ *! *

tɑk *! *

tɑ * *

/sɑk/ [tɑ] 100% of the time *[sɑk], *[sɑ], *[tɑk] 0% of the time /zu/ [du] 100% of the time /dɑg/ [dɑ] 100% of the time…

H-INITIAL *FRICATIVE, *CODA >> IDENT-[CONT], MAX

Stage 1: Making Mistakes

A Grammar

Grammar at Work

Analyzing this error:

/sɑk/ *Fric *Coda Ident[cont] Max

sɑk *! *!

sɑ *! *

tɑk *! *

tɑ * *

H-INITIAL *FRICATIVE, *CODA >> IDENT-[CONT], MAX

*Fric *Coda Ident[cont] Max

sɑk ~ tɑ L L W W

Storing Mistakes

Caching a grammar’s errors


sock sɑk ~ tɑ L L W W

zoo zu ~ du L W

dog dɑg ~ dɑ L W

eyes aɪz ~ aɪ L L W

Learning from Mistakes

Choosing a Cached error


sock sak ~ ta L L W W

zoo zu ~ du L W

dog dɑg ~ dɑ L W


*FRIC >> MAX >> *CODA >> IDENT[CONT]

Learning from Mistakes

Archiving this error:

Re-ranking:

via Biased Constraint Demotion

(BCD:

Prince and Tesar, 2004)

*FRIC, *CODA >> IDENT[CONT] , MAX


dɑg ~ dɑ L W

H-NEW *FRIC >> MAX >> *CODA >> IDENT[CONT]

Result: Slight Improvement

New Grammar At Work


dɑg ~ dɑ L W

/sɑk/ *Fric Max *Coda Ident[cont]

sɑk *! *

sɑ *! *

tɑk * *

tɑ *! *

Stage Two: Cacheing...

Choosing a Cached error


sock sɑk ~ tɑk L W

zoo zu ~ du L W

dog dɑg ~ dɑg

eyes aɪz ~ aɪd L W

MAX >> *CODA >> IDENT[CONT] >> *FRIC

Stage Two: Learning...

Re-ranking via BCD:

*FRIC >> MAX >> *CODA >> IDENT[CONT]


dɑg ~ dɑ L W

sɑk ~ tɑk L W

H-NEW MAX >> *CODA >> IDENT[CONT] >> *FRIC

Result: Success!

Target Grammar At Work


dɑg ~ dɑ L W

sɑk ~ tɑk L W

/sɑk/ Max *Coda Ident[cont] *Fric

sɑk * *

sɑ *! *

tɑk * *!

tɑ *!

Learner Trajectory:

/sɑk/ *Fric *Coda Ident Max

sɑk *! *!

sɑ *! *

tɑk *! *

tɑ * * /sɑk/ *Fric Max *Coda Ident

sɑk *! *

sɑ *! *

tɑk * *

tɑ *! * /sɑk/ Max *Coda Ident *Fric

sɑk * *

sɑ *! *

tɑk * *!

tɑ *!

1b. A Comparable HS Learner

McCarthy (2000, 2008ab, etc.)

See also Staubs and Pater (2012), in prep.

Like OT: input output mappings

markedness/faith constraints

typologically driven

Unlike OT: serial evaluation

multiple derivations

finite candidate set

Mappings in HS via Derivation

/sɑk/ *Fric *Coda Ident [cont]

Max

sɑk *! *!

sɑ *! *

tɑk * *

**ta not in this candidate set!

* *

GEN(/input/) = [cand-1], fully faithful [cand-2], violates Ident[cont] only [cand-3], violates Max only

Upshot: - finite number of candidates - ‘one step away’ from input - candidate space crucially

depends on set of Faith



sɑk *! *!

sɑ *! *

tɑk * *

/tɑk/ *Fric *Coda Ident Max

tɑk *!

tɑ *

sɑk *! * *

If Eval returns unfaithful cand: - [optima] for mapping-n is

now /input/ for mapping-n+1:



sɑk *! *!

sɑ *! *

tɑk * *


tɑk *!

tɑ *

sɑk *! * *

If Eval returns unfaithful cand: - [optima] for mapping-n is

now /input/ for mapping-n+1:

- re-apply GEN...

- note [ta] IS now a candidate!



sɑk *! *!

sɑ *! *

tɑk * *


tɑk *!

tɑ *

sɑk *! * *

/tɑ/ *Fric *Coda Ident Max

tɑ

sɑ *! *

tɑk *!

If Eval returns FAITHful cand: - derivation is finished - [optima] for mapping-n is the

final output winner

Here: /sak/ tak [ta]

Issue: learning multi-step derivations?


sɑk *! *!

sɑ *! *

tɑk * *


tɑk *!

tɑ *

sɑk *! * *


tɑ

sɑ *! *

tɑk *!

How could the learner compare sak ~ ta?

They do not co-exist

in one tableau!

The learner’s error /sak/ tak [ta] What can you take from this error?

Proposal for multi-step derivations


sɑk *! *!

sɑ *! *

tɑk * *

Use the first step

*Fric *Coda Ident Max

sɑk ~ tɑk L W

The HS Cache – Storing First Steps

Grammar

Derivations

Cache: First Steps only


sock sak ~ tak L W

zoo zu ~ du L W

dog dɑg ~ dɑ L W

shoes ʃuz ~ tud L W

*FRIC >> *CODA >> IDENT[CONT] >> MAX

sock /sak/ tak [ta]

zoo /zu/ [du]

dog /dag/ [da]

shoes /ʃuz/ tud [tu]

HS Trajectory: Three Stages


sɑk *! *!

sɑ *! *

tɑk * *


tɑk *!

tɑ *

sɑk *! * *


tɑ

sɑ *! *

tɑk *!

Stage One: /sak/ tak [ta]

/sɑk/ *Coda Ident *Fric Max

sɑk *! *!

sɑ *!

tɑk * *

/sɑ/ *Coda Ident *Fric Max

sɑk *! *

sɑ *

tɑ * *!

(A) Stage Two: /sak/ [sa]

/sɑk/ Max *Coda Ident *Fric

sɑk * *

sa *! *

tɑk * *!

Stage Three /sak/ [sak]

2a. A Serialist Success in Learning

A question: why is grammar change gradual? In both of these OT and HS approaches: - learner only learns from the Archive, not

everything Cached... - so: order of acquisition depends on which errors

get Archived - problem: learning from the wrong errors predicts

particularly weird backtracking

The Error-Selective Learning Idea



zoo zu ~ du L W

dog dɑg ~ dɑ L W


The OT Error Selection Algorithm (Tessier 2007, 2009) – choose errors that are simple – learn one thing at a time

– pick errors with as few Ls as possible...

The Error-Selective Learning Idea

*Fric *Coda *Comp

Onset

*Comp

Coda

Ident [cont]

Max


zoo zu ~ du L W

dog dɑg ~ dɑ L W


strengths stɹɛŋkθs ~ tɛ L L L L W W

The OT Error Selection Algorithm (Tessier 2007, 2009) – choose errors that are simple – learn one thing at a time

Error Selection: Ambiguity!

A Global Approach: Parallel OT One possible learning path:

*Fric *Coda *VOICED VELARSTOP

*VOICED FRIC

Ident[cont] Max


zoo zu ~ du L L W

dog dɑg ~ dɑ L L W

eyes aɪz ~ aɪ L L L W

The OT Error Selection Algorithm (Tessier 2007, 2009) – often, many errors will be tied for fewest Ls – sometimes, the ESA has to pick at random

Error Selection: Ambiguity!


*Fric *Coda *VOICED VELARSTOP

*VOICED FRIC

Ident[cont] Max


zoo zu ~ du L L W

dog dɑg ~ dɑ L L W

eyes aɪz ~ aɪ L L L W

The OT Error Selection Algorithm (Tessier 2007, 2009) – often, many errors will be tied for fewest Ls – sometimes, the ESA has to pick at random

- multiple Ls in an error can cause odd learning paths

Error Selection: Not Selective Enough


To build a grammar in which winner beats loser: - install at least ONE W-preferring constraint above ALL L-preferring constraints

(Prince and Smolensky, 1993/2004; Prince and Tesar, 2004):

Possible Stage 2: Max >> *Fric, *Coda >> Ident[cont]

winner ~ loser *Fric *Coda Ident[cont] Max

sɑk ~ tɑ L L W W

/sɑk/ Max *Fric *Coda Ident

sɑk *! *

sɑ *! *

tɑk * *

tɑ *!

Error Selection: Not Selective Enough


To build a grammar in which winner beats loser: - install W-preferring constraints that resolve the most errors - here: Ident[cont] (Prince and Tesar, 2004; Hayes, 2004)

Resulting Stage 3: Ident >> *Fric, *Coda >> Max

winner ~ loser *Fric *Coda Ident[cont] Max

sɑk ~ tɑ L L W W

sak ~ tak L W

/sɑk/ Ident *Fric *Coda Max

sɑk * *!

sɑ * *

tɑk *!

ta *! *

Stage 1: /sak/ [ta] Stage 2: /sak/ [tak] Stage 3: /sak/ [sa] ...frics improve, codas regress

HS Errors: Always Gradual!

Grammar

Derivations

Cache: First Steps only


sock sak ~ tak L W

shoes ʃuz ~ tud L W

*FRIC >> *CODA >> IDENT[CONT] >> MAX

sock /sak/ tak [ta]

shoes /ʃuz/ tud [tu]

2b. Looking for backtracking

A diary study: Zack (Smith, 2010) Thanks: Philip Dilts (UofA)

This study: For all (2620) two-member CC onsets a) trajectory of 3 cluster types b) additional marked properties Question: Does acquisition of CC onsets ever cause backtracking of other structures? Or vice versa?

Zack: Complex Onset Development

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1;8.1 -2;3.22

2;3.22 -2;5.30

2;6.8 -2;6.15

2;6.16 -2;8.17

2;8.18 -3;2.1

3;2.2 -3;2.27

3;3.1 -3;4.1

3;4.2 -3;8.30

s-stop

stop-r

stop-l

% Faithful Onset Clusters (rather than Reduced)

Codas vs. Stop-/l/-Onsets

17

13 4 10 30

4 4

6

2

30 11 29 65

11 17

48

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1;8.1 -2;3.22

2;3.22 -2;5.30

2;6.8 -2;6.15

2;6.16 -2;8.17

2;8.18 -3;2.1

3;2.2 -3;2.27

3;3.1 -3;4.1

3;4.2 -3;8.30

@stage2: coda obstruents begin to appear

codas vs. stop-/l/ 2;4.15-19 2;6.15-29

glass [da:t] [dra:s]

glasses [da.tɪd] [dra:.siz]

pliers [pae. əd] [pleɪt]

Upshot: As stop-liquid onsets emerge, coda obstruents do not regress

@stage3: stop-/l/ onsets appear

Coda Frics vs. StopLiquid-Onsets

0

26 5

12

3 2

0 0 0

10 2

28

77 13

17 25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1;8.1 -2;3.22

2;3.22 -2;5.30

2;6.8 -2;6.15

2;6.16 -2;8.17

2;8.18 -3;2.1

3;2.2 -3;2.27

3;3.1 -3;4.1 3;4.2 -3;8.30

stop-liq vs. frics @ 2;6 @2;9

Gruff [dʌf] [drʌf]

close [trəud] [trəuz]

please [pid], [piz] [priz]

@stage 3-4: stop liqs onsets emerge

@stage4: coda frics begin to appear accurate

Upshot: As coda fricatives emerge, stop-liquid onsets do not regress

Velar Fronting vs. /s/-Stop Onsets

s-stop vs. velars @ 3;3 @ 3;5

scoop [stu:p] [sku:p]

sky [staɪ] [staɪ], [skaɪ]

stick [stɪt] [stɪt], [stɪk]

79 100 35 154 227 37 34

50

0 0 0 0 0 0 2

73

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1;8.1 -2;3.22

2;3.22 -2;5.30

2;6.8 -2;6.15

2;6.16 -2;8.17

2;8.18 -3;2.1

3;2.2 -3;2.27

3;3.1 -3;4.1

3;4.2 -3;8.30

@stage7: /s/-stop onsets emerge

@stage8: velar fronting begins to cease

Upshot: As velar place emerges, s-stop onsets do not regress

Summary of Serialist Success

HS learner can only see small /i/ [o] changes

So HS learner learns one thing at a time...

... and it knows what it’s learned each time

Upshot: its developmental trajectory doesn’t vacillate qualitatively between faith/repair

Neither, I think, do children


sɑk ~ tɑk L W

sɑk ~ sɑ L W

Known issue for HS learning:

- HS needs more rankings than OT

- ‘Hidden M rankings’: which ensure feeding orders among processes

- How does the error-driven learner find them?

- Phonotactics just got harder! Tessier (2012)

Tessier and Jesney (in prep)

3. A Serialist Failure in Learning

Effects of Markedness Rankings in HS


sɑk *! *!

sɑ *! *

tɑk * *


tɑk *!

tɑ *

sɑk *! * *


tɑ

sɑ *! *

tɑk *!

Ranking: *Fric >> *Coda >> Faith Result: sak tak ta

/sɑk/ *Coda *Fric Ident Max

sɑk *! *

sɑ * *

tɑk *! *

/sɑ/ *Coda *Fric Ident Max

sɑ *!

tɑ *

sɑk *! *

/tɑ/ *Coda *Fric Ident Max

tɑ

sɑ *! *

tɑk *! *

Ranking: *Coda >> *Fric >> Faith Result: sak sa ta

Effects of Markedness Rankings in HS

/sɑk/ *Fric CH ID[cont] ID [place]

sɑk *!

tɑk * *

xɑk *! *

/tɑk/ *Fric CH ID[cont] ID [place]

tak *!

kɑk *

sɑk *! *

/kɑk/ *Fric CH ID[cont] ID [place]

kɑk

tɑk *! *

xɑk *! *

... If process driven by M1 feeds process driven by M2 Example: Consonant Harmony process only for stops Ranking: *Fric >> CH >> Faith /sak/ tak [kak]

When are HS M>>M rankings crucial?

/sɑk/ CH *Fric ID[cont] ID [place]

sɑk *

tɑk *! *

xɑk * *

When are HS M>>M rankings crucial?

Example: Consonant Harmony process only for stops Reverse Ranking: CH >> *Fric >> Faith Result: /sak/ [sak] CH blocks stopping!

/sɑk/ CH *Fric ID[cont] ID [place]

sɑk *!

tɑk *! *

xɑk *! *

kak * *

Parallel Evaluation = No Ordered Processes Any M >> F ranking: CH >> *Fric >> Faith *Fric >> CH >> Faith CH, *Fric >> Faith Result: /sak/ [kak]

In OT: this M >> M ranking not crucial

/sɑk/ *Fric CH ID[cont] ID [place]

sɑk *!

tɑk *! *

xɑk *! *

kak * *

Schematic

Example

A need for hidden M >> M rankings

- M1 process can crucially feed M2 process - M1 and M2 both satisfied in surface forms

M1: *B

M2: *Ab

1st step legal

input: due to M1 surface form:

/AB/ Ab [ab]

output form 2nd step

violating M2 due to M2


- For /AB/ [ab], two-step derivation is needed - Some M1 >> M2 ranking must drive the first step

/AB/ aB

Ab [ab]

Schematic

Example



/AB/ Hidden Ranking:

*B >> Ab

*B

Ab [ab]

*Ab



Schematic

Example




Schematic

Example

*A Hidden ranking:

/AB/ aB *A >> *aB

*aB

[ab]



*aB blocks

/AB/ aB

*Ab

blocks

Ab [ab]

If all rankings are M2 >> M1, /AB/ will surface faithfully!

Schematic

Example

Summary of Serialist Failure

The HS learner sees only single /i/ [o] changes

But to learn a grammar with ordered changes, the HS grammar needs M >> M rankings

And those rankings will not come from evidence

.... So far: I don’t know how the learner should find them all cf. Tessier (2012): get a shovel and look...

cf. Staubs and Pater (2012): ask a different question...

Take Home Messages

A crucial way serial constraint interaction changes learning: • HS shrinks the candidate space • HS simplifies the options at each step

Learner Advantage: gradualness is inherently easier Learner Disadvantage: restrictiveness is inherently harder

• to capture phonotactics as well as alternations, the HS learner must hypothesize unfaithful inputs

• “what if I tried to say */be:n/??” Optimism: the small HS candidate set may help the search for unfaithful inputs, ATB

THANK YOU!

Questions, connections, challenges, complaints…

References

Becker, Michael and Anne-Michelle Tessier (2011). Trajectories of faithfulness in child specific phonology. Phonology 28(2): 163-196.

Campos-Astorkiza, Rebeka (2007). Minimal contrast and the phonology-phonetics interaction. Ph.D., University of Southern California.

Elfner, Emily (2011). Stress-epenthesis interactions in Harmonic Serialism. In John McCarthy and Joe Pater (eds.) Harmonic Grammar and Harmonic Serialism. Equinox Press.

Hayes, Bruce (2004). Phonological Acquisition in Optimality Theory: the early stages. In Kager, Rene, Joe Pater & Wim Zonneveld,(eds.), Fixing Priorities: Constraints in Phonological Acquisition. Cambridge, U.K.: Cambridge University Press

Jesney, Karen (2008). Positional Faithfulness, non-locality, and the Harmonic Serialism solution. In Suzi Lima, Kevin Mullin & Brian Smith (eds.), Proceedings of the 39th Meeting of the North East Linguistics Society (NELS 39). Amherst, MA: GLSA

Jesney, Karen and Anne-Michelle Tessier (2008). Gradual learning and faithfulness: consequences of ranked vs. weighted constraints. In Anisa Schardl, Martin Walkow & Muhammad Abdurrahman (eds.), Proceedings of the 38th Meeting of the North East Linguistics Society (NELS 38), volume 1, 375-388. Amherst, MA: GLSA

Jesney, Karen and Anne-Michelle Tessier (2011). Biases in Harmonic Grammar: the road to restrictive learning. Natural Language and Linguistic Theory 29(1): 251-290

References

McCarthy, John J. 2000. Harmonic Serialism and Parallelism. In M. Hirotani, A. Coetzee, N. Hall and J.-Y. (eds.) Proceedings of NELS 30. Amherst: GLSA: 501-524.

McCarthy, John J. (2007) Hidden Generalizations: Phonological Opacity in Optimality Theory. London: Equinox.

McCarthy, John J. 2008a. The gradual path to cluster simplification. Phonology 25:271-319.

McCarthy, John J. 2008b. The serial interaction of stress and syncope. Natural Language & Linguistic Theory (26)3: 499-546.

Prince, Alan and Paul Smolensky. 1993/2004. Optimality Theory: Constraint Interaction in Generative Grammar. Oxford, UK: Blackwell.

Prince, Alan and Bruce Tesar 2004. Learning Phonotactic Distributions. In R. Kager, W. Zonneveld, J. Pater, (eds.) Fixing Priorities: Constraints in Phonological Acquisition. Cambridge, UK: CUP.

Pruitt, Kathryn. 2010. Serialism and locality in constraint-based metrical parsing. Phonology 27(3): 481-526.Smolensky, P. 1996. On the comprehension/production dilemma in child language. Linguistic Inquiry 27: 720–31.

Smith, Neilson V. (2010). Acquiring Phonology: A cross generational case study. CUP.

Staubs, Robert and Joe Pater (2012). Learning serial constraint-based grammars. In J. J. McCarthy and J. Pater (eds.) Harmonic Grammar and Harmonic Serialism, Equinox.

References

Tessier, Anne-Michelle (2007). Biases and Stages in Phonological Acquisition. Ph.D., Umass Amherst.

Tessier, Anne-Michelle (2009). Frequency of Violation and Constraint-Based Phonological Learning. Lingua 119 (1): 6-41.

Tessier, Anne-Michelle (2010). UseListedError: a grammatical account of lexical exceptions in phonological acquisition. In S. Lima, K. Mullins and B. Smith (eds). Proceedings of NELS39 vol. 2 pp. 813-827. Amherst, MA: GLSA.

Tessier, Anne-Michelle (2012) Error-driven learning in Harmonic Serialism. To appear in the Proceedings of NELS42.

SERIAL VS. PARALLEL PHONOLOGICAL GRAMMARS: Learners...

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