The pattern (Ernestus and Baayen, 2003):
Word-internal obstruents contrast in voicing [vErVEid@n] ’widen-inf’
[vErVEit@n] ’reproach-inf’ Word-final devoicing
[vErVEit] ’widen’ [vErVEit] ’reproach’
Simulating Dutch voicing alternations
The pattern (Ernestus and Baayen, 2003):
Word-internal obstruents contrast in voicing [vErVEid@n] ’widen-inf’
[vErVEit@n] ’reproach-inf’
Word-final devoicing [vErVEit] ’widen’ [vErVEit] ’reproach’
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 18 / 30
Simulating Dutch voicing alternations
The pattern (Ernestus and Baayen, 2003):
Word-internal obstruents contrast in voicing [vErVEid@n] ’widen-inf’
[vErVEit@n] ’reproach-inf’
Word-final devoicing [vErVEit] ’widen’
[vErVEit] ’reproach’
Simulating Dutch voicing alternations
The pattern (Ernestus and Baayen, 2003):
Given a neutralized novel form, Dutch speakers can ’guess’ a voicing for the word-internal obstruent based on lexical trends
% voiced in lexicon % voiced in production
p/b 9% 4%
t/d 25% 9%
s/z 33% 23%
f/v 70% 49%
x/G 97% 80%
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 19 / 30
Simulating Dutch voicing alternations
The pattern (Ernestus and Baayen, 2003):
Given a neutralized novel form, Dutch speakers can ’guess’ a voicing for the word-internal obstruent based on lexical trends
% voiced in lexicon % voiced in production
p/b 9% 4%
t/d 25% 9%
s/z 33% 23%
f/v 70% 49%
x/G 97% 80%
Simulating Dutch voicing alternations
The pattern (Ernestus and Baayen, 2003):
Given a neutralized novel form, Dutch speakers can ’guess’ a voicing for the word-internal obstruent based on lexical trends
% voiced in lexicon % voiced in production
p/b 9% 4%
t/d 25% 9%
s/z 33% 23%
f/v 70% 49%
x/G 97% 80%
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 19 / 30
Simulating Dutch voicing alternations
We used six general constraints (a subset of those used by Ernestus and Baayen pg.20):
*VTV Intervocalic obstruents should be voiced
*VDV Intervocalic obstruents should be voiceless
*P[+voice] Labial stops should be voiceless
*T[+voice] Coronal stops should be voiceless
*S[+voice] Sibilants should be voiceless
*F[-voice] Labial fricatives should be voiced
*X[-voice] Velar fricatives should be voiced
n.b. these constraints are shorthand
Simulating Dutch voicing alternations
We used six general constraints (a subset of those used by Ernestus and Baayen pg.20):
*VTV Intervocalic obstruents should be voiced
*VDV Intervocalic obstruents should be voiceless
*P[+voice] Labial stops should be voiceless
*T[+voice] Coronal stops should be voiceless
*S[+voice] Sibilants should be voiceless
*F[-voice] Labial fricatives should be voiced
*X[-voice] Velar fricatives should be voiced n.b. these constraints are shorthand
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 20 / 30
Simulating Dutch voicing alternations
Each lexical item gets a specific version of one of the most general constraints:
/vErVEid/ *VTV-widen ‘widen’ has intervocalic voicing
/vErVEit/ *VDV-reproach ‘reproach’ does not have intervocalic voicing
Simulating Dutch voicing alternations
Data used:
Trend % Voicing Total forms
p/b voiceless 9% 230
t/d voiceless 25% 719
s/z voiceless 33% 451
f/v voiced 70% 166
x/G voiced 97% 131
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 22 / 30
Simulating Dutch voicing alternations
Simulation details:
Gradient Descent, with 10,000 learning epochs (one epoch: one update over the entire input set) Learning rate: 0.01
Regularization: constraint weights based on a Gaussian prior with µ=0, σ2=100
Simulating Dutch voicing alternations
Results summary:
Real words are correctly voiced/voiceless
% voiced on novel words:
lexicon production (EB) Simulated
p/b 9% 4% 1%
t/d 25% 9% 9%
s/z 33% 23% 18%
f/v 70% 49% 84%
x/G 97% 80% 99%
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 24 / 30
Simulating Dutch voicing alternations
Results summary:
Real words are correctly voiced/voiceless
% voiced on novel words:
lexicon production (EB) Simulated
p/b 9% 4% 1%
t/d 25% 9% 9%
s/z 33% 23% 18%
f/v 70% 49% 84%
x/G 97% 80% 99%
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice]
- Trend-followers: low weights
⇒ concord with *X[-voice]
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 25 / 30
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice] - Trend-followers: low weights
⇒ concord with *X[-voice]
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice]
- Trend-followers: low weights
⇒ concord with *X[-voice]
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 25 / 30
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice]
- Trend-followers: low weights
⇒ concord with *X[-voice]
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice]
- Trend-followers: low weights
⇒ concord with *X[-voice]
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 25 / 30
Simulating Dutch voicing alternations
Weights for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000
-505
*VDVj (exceptions)
- High weight on *X[-voice]
- Exceptions: high weights
⇒ conflict with *X[-voice]
- Trend-followers: low weights
⇒ concord with *X[-voice]
Simulating Dutch voicing alternations
Probabilities for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: voiced
- Trend-followers: correctly voiced - Exceptions: errors towards voicing
- Analogous to Polish stress: exceptions exist, but are prone to regularization
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 26 / 30
Simulating Dutch voicing alternations
Probabilities for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: voiced
- Trend-followers: correctly voiced - Exceptions: errors towards voicing
- Analogous to Polish stress: exceptions exist, but are prone to regularization
Simulating Dutch voicing alternations
Probabilities for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: voiced
- Trend-followers: correctly voiced
- Exceptions: errors towards voicing
- Analogous to Polish stress: exceptions exist, but are prone to regularization
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 26 / 30
Simulating Dutch voicing alternations
Probabilities for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: voiced
- Trend-followers: correctly voiced - Exceptions: errors towards voicing
- Analogous to Polish stress: exceptions exist, but are prone to regularization
Simulating Dutch voicing alternations
Probabilities for forms with x/G: 97% voiced (131 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: voiced
- Trend-followers: correctly voiced - Exceptions: errors towards voicing
- Analogous to Polish stress:
exceptions exist, but are prone to regularization
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 26 / 30
Simulating Dutch voicing alternations
Weights for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000
-505
*VTVj (exceptions)
- Low-ish weight on *S[+voice] - Exceptions: high weights - Trend-followers: lower weights
⇒ but higher than *S[+voice]
Simulating Dutch voicing alternations
Weights for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000
-505
*VTVj (exceptions)
- Low-ish weight on *S[+voice]
- Exceptions: high weights
- Trend-followers: lower weights
⇒ but higher than *S[+voice]
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 27 / 30
Simulating Dutch voicing alternations
Weights for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000
-505
*VTVj (exceptions)
- Low-ish weight on *S[+voice]
- Exceptions: high weights
- Trend-followers: lower weights
⇒ but higher than *S[+voice]
Simulating Dutch voicing alternations
Weights for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000
-505
*VTVj (exceptions)
- Low-ish weight on *S[+voice]
- Exceptions: high weights - Trend-followers: lower weights
⇒ but higher than *S[+voice]
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 27 / 30
Simulating Dutch voicing alternations
Weights for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000
-505
*VTVj (exceptions)
- Low-ish weight on *S[+voice]
- Exceptions: high weights - Trend-followers: lower weights
⇒ but higher than *S[+voice]
Simulating Dutch voicing alternations
Probabilities for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: 20% voiced - Existing words: correct
- Analogous to English stress: each word’s pronounciation is stable
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 28 / 30
Simulating Dutch voicing alternations
Probabilities for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: 20% voiced
- Existing words: correct
- Analogous to English stress: each word’s pronounciation is stable
Simulating Dutch voicing alternations
Probabilities for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: 20% voiced - Existing words: correct
- Analogous to English stress: each word’s pronounciation is stable
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 28 / 30
Simulating Dutch voicing alternations
Probabilities for forms with s/z: 33% voiced (451 total)
0 2000 4000 6000 8000 10000 0.0
- Novel words: 20% voiced - Existing words: correct
- Analogous to English stress: each word’s pronounciation is stable
Summary
Using lexically specific constraints together with more general constraints like *P[+voice], we can model:
1 Probabilistic lexical trends which are reflected in speakers’ treatment of novel words
2 Categorical behavior of extant words in a language’s lexicon
3 Difference between generalizations with few vs. many exceptions
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 29 / 30
Summary
Using lexically specific constraints together with more general constraints like *P[+voice], we can model:
1 Probabilistic lexical trends which are reflected in speakers’
treatment of novel words
2 Categorical behavior of extant words in a language’s lexicon
3 Difference between generalizations with few vs. many exceptions
Summary
Using lexically specific constraints together with more general constraints like *P[+voice], we can model:
1 Probabilistic lexical trends which are reflected in speakers’
treatment of novel words
2 Categorical behavior of extant words in a language’s lexicon
3 Difference between generalizations with few vs. many exceptions
Claire Moore-Cantwell, Joe Pater UMass Amherst OCP Barcelona
Gradient exceptionality in Maximum Entropy Grammar with lexically specific constraints 29 / 30
Summary
Using lexically specific constraints together with more general constraints like *P[+voice], we can model:
1 Probabilistic lexical trends which are reflected in speakers’
treatment of novel words
2 Categorical behavior of extant words in a language’s lexicon
3 Difference between generalizations with few vs. many exceptions