The Learnability of the Accentuation of Sino-Japanese Words
and Loanwords: The Hidden Structure Problem*
Motong Li
Osaka University
ABSTRACT. This paper analyzes the learnability of dominant accentual patterns of Sino-Japanese words andloanwords using the constraint ranking proposed by Li (2017) as the target grammar. Convergence results ofcomputational simulations vary across algorithms and levels of inputs. Learning in the face of hidden structure,learners of Robust Interpretive Parsing (RIP; Tesar and Smolensky 2000) did not converge, indicating the
failure to make full use of available probabilistic information. Instead, two novel parsing strategies proposed by
Jarosz (2013) help to solve related problems of RIP, yielding significant improvements in performance evenwith the relatively complex target grammar.
Keywords: learnability, hidden structure, Gradual Learning Algorithm, Robust Interpretive Parsing, ExpectedInterpretive Parsing

1. Introduction
Learnability deals with the problem of how learners approach, and eventually converge tothe target grammar. In Optimality Theory (OT; Prince and Smolensky 1993/2004)
particularly, learnability asks how learners equipped with an initial constraint ranking update
each constraint position until the final ranking matches the adult one. Compared to general
OT work that focuses on the construction of the adult grammar, learnability is less studied,and less accountable when there are hidden structures like feet that need to be parsed (e.g.
whether [σσ’σ] should be parsed into iambic /(σσ’)σ/ or trochaic /σ(σ’σ)/). Depending on thedata and the algorithm, the attempt to disambiguate sometimes fails, bringing inconsistency
to the whole OT analysis.
Taking several combinations of learning and parsing algorithms into consideration, thispaper conducts a computational study on the learnability of dominant accentual patterns
observed in Sino-Japanese words and loanwords. The main purpose here is to examine theconvergence rate of different algorithms with or without the intervention of the hidden
structure problem, and to present various learning patterns into which the results can beclassified.
2. Multi-level representation
Apoussidou (2007), which studied the learnability of metrical phonology in detail, used athree-level phonological representation for the hidden structure problem. As shown in (1),these three levels, Underlying Form (UF), Surface Form (SF) and Overt Form (OF), arerepresented by vertical bars, slashes and brackets respectively.
(1) Three-level phonological representation (Apoussidou 2007)
UF|σσσ|
Syllables onlySF/(σ’σ)σ/Syllables, foot structures and the stressOF[σ’σσ]
Syllables and the stress
UF only consists of syllables, and can be extracted trivially from SF and OF in
Apoussidou’s analyses of grammatical stress in Latin and Pintupi. SF, which is the output
level of general OT analyses, contains the full structural description, namely syllables, footstructures and the stress. Compared to SF, OF lacks the hidden foot structures but has theremaining overt information, and is hence more realistic considering the process ofperception. Ambiguity emerges when OF is parsed into SF for learners to obtain the footstructure.

― 31 ―

3. Target grammar and dominant accentual patterns
Based on Ito and Mester (2016) which conducted a thorough OT analysis of loanwords,
Li (2017) proposed a constraint ranking that accounts for dominant accentual patterns of both
Sino-Japanese words and loanwords simultaneously, as presented in (2).
(2) Constraint hierarchy as the target grammar1 (Li 2017)

Dominant accentual patterns of the two lexical strata are shown in (3), with foot structure
optimally specified (L: light syllable; H: heavy syllable; #: morpheme boundary; +: lexeme
boundary; boldface: foothead).
(3) Dominant accentual patterns (Li 2017)a. Sino-Japanese words and loanwords
(L’)
(L’L)
(H’)
b. Sino-Japanese words(L’)#L(L)#(LL)(L’L)#L(H)#(LL) (LL)#(H)(H)#(H)c. Loanwords(L’L)L(H’)L(L’)H
L(H’)L(LL)(L’L)L(LL)L(L’L)L(H)(L’)H L(L’L)H
L(H’)H(H)+(H)(LL)+(L)(LL)+(H)(H’)#L(LL)#(LL)(L)#(H)(LL)(LL)(H’)H
L(H)(LL)(H)(H’)L(H)(LL)(H)L(L’L)L(L’L)H(LL)(LL) (L’L)LIn order to explain the different accentual distribution in these lexical strata, the analysis
in Li (2017) highly relied on constraints such as quantity-sensitive NONFIN(L) and morphophonological LEXFT and MORFT, which all referred to the foot structure in particular ways.
The complexity of foot specification makes it even harder to parse the hidden structure as
expected in an ambiguous condition.
4. Algorithms
This section briefly introduces two learning algorithms, Error-Driven Constraint
Demotion (EDCD; Tesar and Smolensky 2000) and Gradual Learning Algorithm (GLA;
Boersma 1997, 1999; Boersma and Hayes 2001), and the parsing algorithm Robust
Interpretive Parsing (RIP; Tesar and Smolensky 2000), which are all widely used inlearnability-related literature.
4.1 EDCDEDCD is a learning algorithm based on original OT which has discrete ranks. Thisalgorithm assumes that, given a set of grammatical structural descriptions as inputs and aconstraint hierarchy Hstart as the initial grammar, learners use their current grammar H to
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select the optimal candidate, and compare it with the real data. If the current optimal output
loser does not match the real data winner, then the deciding constraints that favor the loser
should be minimally demoted below constraints that favor the winner, resulting in a new
constraint hierarchy Hnew. Learners will use Hnew to update their current grammar and apply it
to the next input. This learning process will not stop until all optimal outputs produced by thelearner’s grammar match the real data.
4.2 GLAGLA is based on stochastic OT (Boersma 1997, 1999; Boersma and Hayes 2001), which
is aimed at modeling free grammatical variation and speech errors that are frequently seen
during the early learning stage. Unlike the discrete ranking in original OT, the ranking instochastic OT is continuous, with each constraint numerically assigned a ranking value. At
evaluation time, the evaluator adds a Gaussian random variable (mean = 0, standard deviation= evaluation noise) to each constraint to temporally randomize their current ranking value.
An example from Boersma and Hayes (2001) is shown in (4). Constraints C2 and C3 havetheir own distribution of ranking values. At evaluation time, it is possible to choose theselection points within the range of these two constraints. Generally as in (4a), selection
point ･2 is ranked higher than ･3, resulting in C2 ≫ C3 with high probability, but if ･2 and ･3
are randomized as in (4b), then C3 would outrank C2.
(4) Stochastic evaluation (Boersma and Hayes, 2001)
a. Common result: C2 ≫ C3

b. Rare result: C3 ≫ C2

Unlike EDCD, whose update rule only permits minimal demotion, GLA often demotes
constraints that favor the loser and promotes constraints that favor the winner symmetrically.
The range of demotion and promotion is decided by the parameter plasticity (ε). If ε is set to1, then the ranking value should be added or subtracted by 1 regarding whether the constraint
favor the winner or the loser. As reported in Boersma (2003), GLA performs better than
EDCD when the problem of hidden structures gets involved2.
4.3 RIP
RIP is a representative parsing algorithm in OT for learners to build hidden structuresfrom overt information. An example from Apoussidou (2007) is shown in (5). Compared togeneral OT work whose input level is often UF, the input level of RIP is OF, which meansthat overt information like stress or accent is incorporated in it.
Candidates in RIP are
represented in SF, but all restricted to those whose OF is identical to the input. Learners thenpick one candidate ☞RIP as optimal based on their current grammar (ALLFT-L ≫ ALLFT-R ≫TROCHAIC ≫ IAMBIC in (5)), and interpret it as the winner for the current data.

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(5) RIP in OT (Apoussidou 2007)
ALLFT-L
OF: [σσ’σ]*!
a. /σ(σ’)σ/ [σσ’σ]☞RIP b. /(σσ’) σ/ [σσ’σ]*!
c. /σ(σ’σ)/ [σσ’σ]

ALLFT-R

TROCHAIC***IAMBIC*

Next, as shown in (6), learners do general OT evaluation with UF as the input and an
unlimited candidate set, pick one candidate ☞VP as optimal, and compare it with ☞RIP. If ☞VPand ☞RIP are different from each other, then the update rule based on EDCD (TROCHAIC
demoted below IAMBIC) or GLA (TROCHAIC demoted and IAMBIC promoted numerically) is
applied. If they match each other, then the current learning step ends and the algorithm moves
to another datum.
(6) Error detection, given underlying form (Apoussidou 2007)
ALLFT-R
TROCHAICALLFT-L
UF: |σσσ|*!
*a. |σσσ| /σ(σ’)σ/ [σσ’σ]**!
☞RIP b. |σσσ| /(σσ’)σ/ [σσ’σ]*!
c. |σσσ| /σ(σ’σ)/ [σσ’σ]*☞VP d. |σσσ| /(σ’σ)σ/ [σ’σσ]*!
*e. |σσσ| /σ(σσ’)/ [σσσ’]**!
f. |σσσ| /(σ’)σσ/ [σ’σσ]*!*g. |σσσ| /σσ(σ’)/ [σσσ’]

IAMBIC**In the next two sections, computational simulations are carried out to examine theconvergence rate in different situations where the input level (SF or OF) and the learningalgorithm (EDCD or GLA) are taken into consideration.
5. Simulations: without parsing
This section conducts simulations with SF as the input level, which means the winners’
optimal foot structures are known to learners without parsing. Training data are shown in (3)with full structural description. Praat (version 6.0.19; Boersma and Weenink 2016) is used tocarry out all simulations in this paper.
5.1 EDCD
The parameter settings for EDCD learners are specified in (7). The update rule and theinitial ε mean that ranking values of deciding constraints that favor the loser are minimallydemoted below constraints that favor the winner by one. Due to the discreteness of original
OT, the value of ε here does not affect the actual ranking. Because variation of ranking values
is not allowed in EDCD, evaluation noise is set to 0. The number of ε assigned 1 indicatesthat ε is kept to 1 during the whole learning process. Replications per ε set to 1,000 stipulates
that 1,000 learning steps are carried out for the current ε. After 1,000 learning steps, the result
is counted as one simulation for one learner. Twenty learners are simulated here, and theinitial ranking values of constraints are all set to 100. After the whole learning process,
100,000 SF data are randomly generated based on the learner’s final grammar, and arecompared with the actual OF data shown in (3) to calculate the accuracy rate without
considering the foot structure. The algorithm is considered to have converged successfully
only if the learner’s accuracy rate is 100%.

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(7) parameter settings for EDCD learners
Update rule
EDCD

Evaluation noise0

Initial ε1

Number of ε1

Replications per ε
1,000

As a result, all EDCD learners converged to the target grammar (convergence rate =100%), the effect of which was proven in Tesar and Smolensky (2000). Learners’ final
rankings can be classified into two patterns. Ten learners belong to the first pattern, Linguist
Analysis (PLA), which is identical to the target constraint hierarchy shown in (2). Figure 1
presents the ranking dynamics of this pattern. From this we can see that the learning processeffectively stops at about the 125th learning step, after which ranking values do not change
anymore.
The second pattern, Crucial Ties (PCT; 10 learners), treats two or more constraints withtied ranks as one constraint and counts their violation marks jointly. An example for learners
whose INITFT and WDACC ranked equally is given in (8). The unaccented (8a) and antepenultimate-mora-accented (8b) violate WDACC and INITFT respectively. Because of theequal rank of these two constraints, violation marks here are canceled out, bringing thecompetition to the final-rank PARSE-σ. However, as pointed out by Apoussidou (2007) and
McCarthy (2008), the strategy of crucial ties does not have enough evidence to prove itself
valid. Turning back to (8), it is implausible to consider that not parsing the initial syllable into
a foot is as disadvantageous as not having an accent.
(8) Crucial Ties: WDACC and INITFT

*

*
* *

*

PARSE-σ

*

WDACC

*

INITFT*WSPNONFIN(FT’)

FTBIN

NONFIN(σ)

MORFT

NONFIN(L)

RIGHTMOST☞ a. /(LL)(LL)/b. /L(L’L)L/

NOLAPSEMINWDACC

MT

LEXFT

|LLLL|(/amerika/ ‘America’)**!**

Figure 2 GLA, PLA, without parsing

Figure 1 EDCD, PLA, without parsing

5.2 GLAThe parameter settings for GLA learners are specified in (9). The update rule set toSymmetrical all means that constraints that favor the loser are demoted, and constraints thatfavor the winner are promoted symmetrically. In order to make use of stochastic knowledge,
evaluation noise is assigned a value of 2. The number of ε is set to 4 and each of them areused 10,000 times (40,000 times in total). To model different learning speeds in different
learning stages, the decrement of ε is set to 0.1, which means that the first ε is 1, the second― 35 ―

0.1, the third 0.01, and the forth 0.001. Other parameter settings are the same as those in
Section 5.1.
(9) parameter settings for EDCD learners
Update rule
Symmetric all

Evaluation noise
2

Initial ε1

Number of ε4

Replications per ε
10,000ε decrement
0.1

GLA learners also learned the target grammar successfully (convergence rate = 100%),
and they all belong to PLA. The ranking dynamics of one representative learner are shown in
Figure 2, where constraint demotion and promotion can both be observed. In the earlylearning stage, ε has a big value and learning errors occur frequently, which makes the
ranking dynamics fluctuate greatly. But as learning proceeds, ε becomes smaller and learning
errors decrease, resulting in near-stable ranking values.
6. Simulations: parsing with RIPThis section runs simulations with OF as the input level, which is more realistic
considering the process of perception. In order to get a full structural description for the input,
learners must make use of their parsing algorithm, at the risk of wrongly guessing a hidden
structure that does not match the expected one. Here EDCD and GLA learners are equippedwith RIP, with the same training data (foot structure removed) and parameter settings as in
Section 5. Basic statistics of the accuracy rate of these two algorithms are given in (10).
(10) Descriptive statistics of EDCD/RIP and GLA/RIP
Algorithm
EDCD/RIP
GLA/RIP

N
20
20

Mean
0.89
0.92

SD
0.15
0.01

Median
0.98
0.92

Min
0.5
0.92

Max
0.98
0.94

Compared to the guaranteed convergence illustrated in Section 5, neither EDCD/RIP nor
GLA/RIP learners converged to the target grammar. A statistical difference was not foundbetween these two algorithms (Mann-Whitney U test, U = 157.5, p = .245).
6.1 EDCD/RIP
Results of EDCD/RIP learners can be classified into two patterns. The first pattern,
PNF(F’)/IF↑, has twelve learners with the reversed partial ranking NONFIN(FT’), INITFT ≫ FTBIN.
The linear ranking of this pattern is shown in (11), with |LLLH| as its input. (11b) and (11c)have identical violation marks, hence the chance for each of them to be selected as optimal isnearly fifty-fifty. When (11c) is randomly chosen, the accuracy rate decreases because the OFof (11c) does not match the real data.
(11) PNF(F’)/IF↑: antepenultimate- and pre-antepenultimate-mora accentuation

* *
* * *
*
*

PARSE-σ

*

WDACC

*

FTBIN

*
*

WSP

NONFIN(σ)

*!

MORFT

NONFIN(L)

RIGHTMOSTNOLAPSENONFIN(FT’)

MINWDACC

MT

LEXFT

a. /L(L’L)H/☞ b. /(L)(L’L)H/☞ c. /(LL)(L’)H/

INITFT|LLLH|(/are’rugiː/ ‘allergy’)

**
* *
*

The remaining eight learners belong to PSLUMP, whose ranking values of several
constraints fall sharply. Accuracy rates of this pattern vary greatly, with a minimum of 50%.
The ranking dynamics of one representative learner are shown in Figure 3, where the slump
of FTBIN, PARSE-σ, RIGHTMOST and WDACC can be clearly observed. Due to the lowest rank
― 36 ―

of WDACC, words consisting of three or more moras will be output without an accent in thisranking, which cannot reflect the accented part in the real data correctly.
6.2 GLA/RIP
Most of the GLA/RIP learners have the reversed partial ranking MORFT ≫ NONFIN(L)and INITFT, NONFIN(FT’) ≫ FTBIN (PMF/NF(F’)/IF↑). This pattern also gives |LLLH| two optimalcandidates, and wrongly outputs |H#L| (/kyuː’#yo/ ‘salary’) and |LL#L| (/ko’ku#chi/‘notification’) as /(H)#(L)/ and /(LL)#(L)/ without an accent. The ranking dynamics of thispattern are shown in Figure 4, where a fluctuating rise of WDACC and RIGHTMOST can beobserved, with the ε decrement set to 0 (the result is the same as the ε decrement set to 0.1).

Figure 3 EDCD/RIP, PSLUMP

Figure 4 GLA/RIP, PMF/NF(F’)/IF↑ (ε decrement = 0)

7. Reexamination of RIP
The convergence failure of EDCD/RIP and GLA/RIP learners can be partly attributable tothe parsing algorithm. This section introduces Jarosz (2013), which gave a detailed reexamination of RIP and proposed two alternatives, Resampling Robust Interpretive Parsing(RRIP) and Expected Interpretive Parsing (EIP).
7.1 RRIP
In order to highlight the problem of RIP, the algorithm of RIP reformulated by Jarosz(2013) is shown in (12), which does not affect the behavior of the original parsing algorithm.
(12) Reformulated RIP for GLA (Jarosz 2013)
1. Initialize Stochastic Grammar: G0
2. Iterate over d in D:
a. Sample G′ ~ Gi
b. Input = uf(d)c. Output = OptimizeG′(Input)
d. If overt(Output) ≠ d:i. Parse = RIPG′(d)ii. Gi+1 = Update(Gi, Parse, Output)
Jarosz stated that “[F]rom the reformulation it is clear that parsing is only relevant in casethe selected grammar G′ generates an error…What is odd about this use of interpretive
parsing in the stochastic setting, then, is that the learner nonetheless uses the known-to-beincorrect G′ for interpretive parsing.” In order to solve this problem, Jarosz added a simple
modification to RIP, as shown in (13), with step 1 to 2c identical to those in (12). The
difference between RRIP and RIP is that if G′ generates an error, the learner simply― 37 ―

resamples another grammar G″ and uses it for interpretive parsing. This helps the learnerreference its stochastic grammatical knowledge, the advantage of which is confirmed using
simulations in Section 8.1.
(13) RRIP for GLA (Jarosz 2013)…
2d. If overt(Output) ≠ d:i. Sample G″ ~ Gi
ii. Parse = RIPG″(d)iii. Gi+1 = Update(Gi, Parse, Output)
7.2 EIPThe second problem pointed out by Jarosz (2013), the parsing-production mismatch, is
exemplified in (14). Given the assumed ranking values, ALLFT-L ≫ ALLFT-R ≫ TROCHAIC≫ IAMBIC and ALLFT-R ≫ ALLFT-L ≫ TROCHAIC ≫ IAMBIC will be generated stochasticallywith equal probability, resulting in (14a) and (14c) being output as optimal nearly 50% of thetime. TROCHAIC ≫ IAMBIC is fully activated to disfavor (14b) and (14d).
(14) The parsing-production mismatch (Jarosz 2013)|LLL|
a. /(L’L)L/b. /(LL’)L/c. /L(L’L)/
d. /L(LL’)/

ALLFT-L
300

ALLFT-R
300

TROCHAIC
200

*
*

*

IAMBIC
100
*

*
*

*
*

However, if the learner hears [LL’L] and has to parse it with its current grammar, thencandidates for RIP are restricted to (14b) and (14c) whose OF is identical to the real datum.
This time, which candidate will be selected as the optimal SF depends in whole on therelative rank of ALLFT-R and ALLFT-L, leaving TROCHAIC and IAMBIC inactivated. In
Jarosz’s words, “[A]ccording to the learner’s current grammar, /(LL’)L/ is the only possible
parse of [LL’L], but RIP fails to reflect this categorical restriction imposed by the grammar.”
In order to solve this problem, Jarosz proposed EIP, as shown in (15). With step 1 to 2c
unchanged, when an error is detected, EIP repeatedly resamples new grammars from thecurrent one until the OF of the output matches the real datum d.
(15) EIP for GLA (Jarosz 2013)…
2d. If overt(Output) ≠ d:i. Parse ~ P(parse | Gi, d)
ii. Gi+1 = Update(Gi, Parse, Output)
8. Simulations: parsing with RRIP and EIP
Based on Jarosz’s proposition, this section conducts simulations using the novel parsing
strategies RRIP and EIP. Because these algorithms require stochastic knowledge, the learningalgorithm here is restricted to GLA. Training data and other parameter settings are the sameas in Section 6. Basic statistics of the accuracy rate are given in (16), which includes theabove-mentioned results of GLA/RIP for comparison.

― 38 ―

(16) Descriptive statistics of GLA/RIP, GLA/RRIP and GLA/EIP
Algorithm
GLA/RIP
GLA/RRIP
GLA/EIP

N
20
20
20

Mean
0.92
0.94
1

SD
0.01
0.02
0

Median
0.92
0.94
1

Min
0.92
0.94
1

Max
0.94
1
1

A statistical difference is found between these three algorithms (Kruskal-Wallis H test, H= 48.627, p < .001), and the result for Bonferroni-corrected pairwise comparisons is
significant (Mann-Whitney U test, p < .001 for all three pairs).
8.1 GLA/RRIP
Three patterns are observed in GLA/RRIP learners. Two learners belong to PLA,
converging to the target grammar successfully. The second pattern (PMF/NF(F’)↑; 17 learners)
has the reversed partial ranking MORFT ≫ NONFIN(L) and NONFIN(FT’) ≫ FTBIN, which
wrongly parses |H#L| and |LL#L| into /(H)#(L)/ and /(LL)#(L)/ with WDACC and RIGHTMOST
fluctuating relentlessly as presented in Figure 4. The last learner has the third pattern PFB↑
where FTBIN outranks NONFIN(σ), NOLAPSE and MT, parsing |LH| (/ri’bon/ ‘ribbon’) and|HLH| (/rande’buː/ ‘rendezvous’) into /(LH)/ and /(H’L)H/ with a trimoraic foot.
To summarize, although two learners fortunately acquired the target grammar relying on
their random resampling strategy, the factthat 90% of the learners failed to reach thegoal still calls the validity of RRIP into
question.
8.2 GLA/EIPAs shown in (16), all GLA/EIP
learners successfully converged with their
outputs all matching the real data in the
OF level. Twelve learners are classified
into PLA, and the remaining eight learners
belong to the pattern PNF(F’)↑ withNONFIN(FT’) outranking FTBIN. The Figure 5 GLA/EIP, P
NF(F’) ↑ (ε decrement = 0)
ranking dynamics of PNF(F’)↑ are shown in
Figure 5, with the ε decrement set to 0. The only difference between PNF(F’)↑ and PLA is theoutput of |LL| (/me’mo/ ‘note’). In PLA, |LL| is parsed into /(L’L)/ with a bimoraic footstructure due to the effect of FTBIN, while in PNF(F’)↑ /(L’)L/ is output as optimal, leaving theword-final mora unparsed to avoid violating NONFIN(FT’), as shown in (17). However,
because these two outputs have the same OF [L’L], learners of these two patterns cancommunicate without any hindrance. Hence the result of PNF(F’)↑ is also counted as converged.
Moreover, the final value of NONFIN(FT’) and FTBIN in PNF(F’)↑ are almost identical, so it ishighly probable for PNF(F’)↑ to shift to PLA as learning proceeds.
(17) PNF(F’)↑: monomoraic-foot /(L’)L/ as optimal
PARSE-σ*

WDACC― 39 ―

* *!

WSP

*

INITFT*FTBINRIGHTMOSTNONFIN(FT’)

NONFIN(σ)

NOLAPSEMT

MINWDACCMORFTNONFIN(L)☞ a. /(L’)L/b. /(L’L)/

LEXFT

|LL|
(/me’mo/ ‘note’)***9. Conclusions
This study carried out several computational simulations to examine the learnability of
dominant accentual patterns of Sino-Japanese words and loanwords. With a full structuraldescription incorporated in the input level of perception, both EDCD and GLA learners
converged efficiently. However, under more realistic conditions, the ambiguity of hidden
structures cannot be effectively tackled by RIP. RRIP and EIP proposed by Jarosz (2013)
were introduced to overcome the problem of RIP. Finally, GLA/EIP learners all successfully
converged, showing that EIP can take full advantage of probabilistic information even in theface of an intricate target grammar. Future work will mainly cover the examination ofdifferent parameter settings and update rules, and the simultaneous learning-parsing
algorithm for both the lexicon and the grammar.
Notes

This paper is a revised version of a presentation at the Phonology Spring Meeting 2017, Keio University, on 23
June 2017. I really appreciate the participants who gave feedback to me, as well as two anonymous reviewers
who commented on this paper.
1
Definitions of constraints used in this paper are given below. LEXFT and NONFIN(L) were introduced in Li(2017), while other constraints were defined or referenced in Ito and Mester (2016).
LEXFT: Every lexeme minimally projects its own foot; MORFT: Every lexical morpheme minimally projects its
own foot; MT: Feet are (H), (LL), and (L); NONFIN(σ): Word-final syllables are not footheads; NONFIN(L):
Word-final light syllables are not footheads; NOLAPSE: Syllables are maximally parsed into feet; MINWDACC:
A minimal prosodic word contains a prominence peak; RIGHTMOST: Violated by any foot following the headfoot within the prosodic word; WSP: Heavy syllables are footheads; FTBIN: Feet are minimally binary at somelevel of analysis (mora, syllable); IINTFT: A prosodic word begins with a foot; NONFIN(FT’): Violated by any
head foot that is final in its PrWd; WDACC: A prosodic word contains a prominence peak; PARSE-σ: Allsyllables are parsed into feet.
2
Tesar and Smolensky (2000) prepared 124 target grammars for their EDCD/RIP simulation, and only 75 ofthem were learned successfully (convergence rate = 60%). Using the same data set as Tesar and Smolensky(2000), Boersma (2003) reported that GLA/RIP converged on 70% of the target grammars.
*

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