From Syllables to Syntax:Investigating Staged Linguistic Development through              Computational Modelling  Kris Ja...
Staged Language Acquisition• Language acquisition is consistently described in stages• Lexical and syntactic acquisition s...
Staged Language Acquisition• Language acquisition is consistently described in stages• Lexical and syntactic acquisition s...
Lexical Acquisition• Siskind• Steels• Regier     Siskind (1996)             • Cross-situational analysis               – R...
Lexical Acquisition• Siskind• Steels• Regier     Steels (2001)             • Language games               – Social pressur...
Lexical Acquisition• Siskind• Steels• Regier     Regier (2005)             • Associative learning               – Fast-map...
Syntactic Acquisition• Roy• Elman• Kirby            Roy (2002)                   • Trained a grounded robot to play a     ...
Syntactic Acquisition• Roy• Elman• Kirby               Elman (1993)                      • Incremental Learning           ...
Syntactic Acquisition• Roy• Elman• Kirby                       Kirby (2002)                              • Iterated Learni...
QuestionCan we develop a unified model that performs   staged language acquisition where:  1. The learning mechanisms are ...
Bridging the Gap          between Words and Syntax• Jack, Reed, and Waller (2004)   – Shift from holophrastic to syntactic...
Training Data• Played the Scene Building Game  – Based on the Miniature Language Acquisition    Problem (Feldman et al., 1...
Training Data• Played the Scene Building Game  – Based on the Miniature Language Acquisition    Problem (Feldman et al., 1...
Training Data• Played the Scene Building Game  – Based on the Miniature Language Acquisition    Problem (Feldman et al., 1...
Training Data• Played the Scene Building Game  – Based on the Miniature Language Acquisition    Problem (Feldman et al., 1...
Training Data• Played the Scene Building Game  – Based on the Miniature Language Acquisition    Problem (Feldman et al., 1...
Training Data• The task was surprisingly complex    – Linguistically    – Conceptually• An artificial language was constru...
Initial Assumptions• Joint attention is established at around one-year-old  (Tomasello, 1995)• Receives <event, descriptio...
Initial Assumptions• Sensitivity to data     – Children can identify objects through displacement during       motion (Kel...
Initial Assumptions• Sensitivity to data     – Children show sensitivity to the relative spatial       relationships betwe...
Initial Assumptions• Sensitivity to data     – Children can perform analogies (Gentner and Medina,       1998){<red, (1)>,...
Initial Assumptions• Sensitivity to data     – Children can determine transitional probabilities between       syllables (...
The Model• Training the model  – The Lexical Analysis Unit     • Discovers string-meaning associations  – The Syntactic An...
The Lexical Analysis Unit<event, description> pairs are compared through  a form of cross-situational analysis <event, des...
The Lexical Analysis UnitFeature tuple comparisons are value sensitive and object  identifier insensitive. Two feature tup...
The Lexical Analysis UnitCo-occurring syllable sequences are found <event, description>#1       {<red, (1)>, <circle, (1)>...
The Lexical Analysis Unit New <feature tuple set, description> pairs are  derived     <event, description>#1             {...
The Lexical Analysis Unit• Cross-situational analysis can produce pairs that share  the same strings (homonyms) or the sam...
The Lexical Analysis Unit• From all merged pairs, homonyms are removed by  selecting the most probable feature set for eac...
The Syntactic Analysis Unit• Compositional relationships are found by  combining and comparing lexical items• Lexical item...
The Syntactic Analysis UnitA lexical item triple can be made to express a    rule by:1.   Converting lexical items into ph...
The Syntactic Analysis UnitA lexical item triple can be made to express a    rule by:1.   Converting lexical items into ph...
The Syntactic Analysis UnitA lexical item triple can be made to express a    rule by:1.   Converting lexical items into ph...
The Syntactic Analysis UnitA lexical item triple can be made to express a    rule by:1.   Converting lexical items into ph...
The Syntactic Analysis UnitRules that modify object identifiers can be constructed        <{<red, (2)>}, “a red”>         ...
The Syntactic Analysis UnitRules can be merged when they share transformations                                            ...
The Syntactic Analysis UnitRules can be merged when they share transformations                                            ...
The Syntactic Analysis Unit  Rules can be merged when they share   transformations                                        ...
Comprehension• The model is tested for evidence of language  acquisition through comprehension tasks• The model can compre...
Comprehension• Example 1. Comprehension of “cir cle”  – Find “cir cle” in a phrasal category  – Attempt to create “cir cle...
Comprehension• Example 1. Comprehension of “cir cle”  – Find “cir cle” in a phrasal category  – Attempt to create “cir cle...
Comprehension• Example 1. Comprehension of “cir cle”  – Find “cir cle” in a phrasal category  – Attempt to create “cir cle...
Comprehension• Example 1. Comprehension of “cir cle”  – Find “cir cle” in a phrasal category  – Attempt to create “cir cle...
Comprehension• Example 1. Comprehension of “cir cle”  – Find “cir cle” in a phrasal category  – Attempt to create “cir cle...
Comprehension• Example 2. Comprehension of “red dia mond”  – Find “red dia mond” in a phrasal category  – Attempt to creat...
Comprehension• Example 2. Comprehension of “red dia mond”  – Find “red dia mond” in a phrasal category  – Attempt to creat...
Comprehension• Example 2. Comprehension of “red dia mond”  – Find “red dia mond” in a phrasal category  – Attempt to creat...
Comprehension • Example 2. Comprehension of “red dia mond”      – Find “red dia mond” in a phrasal category      – Attempt...
Comprehension• Example 2. Comprehension of “red dia mond”  – Find “red dia mond” in a phrasal category  – Attempt to creat...
Comprehension• Example 2. Comprehension of “red dia mond”  – Find “red dia mond” in a phrasal category  – Attempt to creat...
Comprehension    • Phrasal categories are substitutable for one      another if they share a subset relationship          ...
Comprehension• Phrasal categories are substitutable for one  another if they share a subset relationship              {<{<...
Comprehension• Phrasal categories are substitutable for one  another if they share a subset relationship            {<{<re...
Results• Observing the developmental shift from lexical to syntactic comprehension    – Tested for comprehension of colour...
Results• Comprehension of colours and shapes compared to colour shape  combinations    – Tested for lexical comprehension ...
ConclusionsThe model demonstrates staged linguistic acquisition  – No maturational triggers are employed  – Training data ...
ConclusionsThe model demonstrates staged linguistic acquisition  – No maturational triggers are employed  – Training data ...
Thank you
Upcoming SlideShare
Loading in...5
×

From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling

303

Published on

This presentation was given at the 28th Annual Conference of the Cognitive Science Society, 2006.

It presents a new model of early stage language acquisition, going from the emergence of first words to syntactic rules.

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
303
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
2
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

From Syllables to Syntax: Investigating Staged Linguistic Development through Computational Modelling

  1. 1. From Syllables to Syntax:Investigating Staged Linguistic Development through Computational Modelling Kris Jack, Chris Reed, and Annalu Waller [kjack|chris|awaller]@computing.dundee.ac.uk Applied Computing, University of Dundee, Dundee, DD1 4HN, Scotland
  2. 2. Staged Language Acquisition• Language acquisition is consistently described in stages• Lexical and syntactic acquisition strategies must operate within a unified model• The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension• Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
  3. 3. Staged Language Acquisition• Language acquisition is consistently described in stages• Lexical and syntactic acquisition strategies must operate within a unified model• The Model – Training Data – Initial Assumptions – Lexical Acquisition – Syntactic Acquisition – Comprehension• Results Holophrastic Early Multi- Pre-linguistic Stage Late Multi-word Stage Abstract Stage Stage word Stage 0 months 6 months 12 months 18 months 24 months 30 months 36 months 42 months
  4. 4. Lexical Acquisition• Siskind• Steels• Regier Siskind (1996) • Cross-situational analysis – Relationship between the appearance and words and their referents
  5. 5. Lexical Acquisition• Siskind• Steels• Regier Steels (2001) • Language games – Social pressure to communicate within a community of agents can lead to an emergent and shared vocabulary
  6. 6. Lexical Acquisition• Siskind• Steels• Regier Regier (2005) • Associative learning – Fast-mapping – Shape bias • No mechanistic changes – Selective attention
  7. 7. Syntactic Acquisition• Roy• Elman• Kirby Roy (2002) • Trained a grounded robot to play a ‘show-and-tell’ game – Training data were divided intoData 0>t>x simple and complex descriptions x>t>yData Model y>t>zData
  8. 8. Syntactic Acquisition• Roy• Elman• Kirby Elman (1993) • Incremental Learning – Mechanistic changes can lead to changes in behaviour t>0 Module t>x ModelData Module t>y Module
  9. 9. Syntactic Acquisition• Roy• Elman• Kirby Kirby (2002) • Iterated Learning – Languages with increasing complexity can emerge across generations of agents Data DataModel Model Model
  10. 10. QuestionCan we develop a unified model that performs staged language acquisition where: 1. The learning mechanisms are constant AND 2. Exposure to training data is constant?
  11. 11. Bridging the Gap between Words and Syntax• Jack, Reed, and Waller (2004) – Shift from holophrastic to syntactic language – The shift was unrealistic as it appeared very early • A form of substitution was employed (similar to Harris (1966); Wolff (1988); Kirby (2002); van Zaanen (2002)) • If the model encountered A B and A C then B and C were considered substitutable for one another – Given the two rules: » S/eats(john, cake) → johneatscake » S/eats(mary, cake) → maryeatscake – Three rules were derived: » S/eats(x, cake) → N/x eatscake » N/john → john » N/mary → mary • This is a reasonable, yet powerful, form of syntactic learning – The target language was unrealistically simple (two-word sentences)
  12. 12. Training Data• Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description → → → t=1 t=2 t=3 t=4
  13. 13. Training Data• Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a red square has appeared → → → t=1 t=2 t=3 t=4
  14. 14. Training Data• Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description a pink cross to the upper right of the red circle → → → t=1 t=2 t=3 t=4
  15. 15. Training Data• Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event socross onsomeone else a blue that can recreate the event based otherthe of the on side description the red circle → → → t=1 t=2 t=3 t=4
  16. 16. Training Data• Played the Scene Building Game – Based on the Miniature Language Acquisition Problem (Feldman et al., 1990) – Aim; describe a visual event so that someone else can recreate the event based on the description another red circle under the pink cross → → → t=1 t=2 t=3 t=4
  17. 17. Training Data• The task was surprisingly complex – Linguistically – Conceptually• An artificial language was constructed based on a simplified problem – Describes the appearance of the second object in a scene – Retained the determiner distinction – Can create sentences such as “a red square a bove the green cir cle” and “a blue tri ang gle to the low er left of the pink star” S = NP1 REL NP2 REL = REL1 | REL2 NP1 = a NP REL1 = a bove | be low | to the REL4 NP2 = the NP REL2 = REL3 REL4 NP = COLOUR SHAPE REL3 = to the low er | to the u pper REL4 = left of | right of COLOUR = black | blue | grey | green | pink | SHAPE = cir cle | cross | dia mond | heart | rec black | red | white tang gle | star | square | tri ang gle
  18. 18. Initial Assumptions• Joint attention is established at around one-year-old (Tomasello, 1995)• Receives <event, description> pairs – An event is a set of six feature tuples – A description is a string{<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  19. 19. Initial Assumptions• Sensitivity to data – Children can identify objects through displacement during motion (Kellman et al., 1987). – Children can use shape and colour to differentiate between objects (e.g. Landau et al., 1988){<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  20. 20. Initial Assumptions• Sensitivity to data – Children show sensitivity to the relative spatial relationships between objects, making distinctions between left and right, and above and below (Quinn, 2003){<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  21. 21. Initial Assumptions• Sensitivity to data – Children can perform analogies (Gentner and Medina, 1998){<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  22. 22. Initial Assumptions• Sensitivity to data – Children can determine transitional probabilities between syllables (Saffran, Aslin, and Newport, 1996){<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} → t=1 t=2 “a pink cross to the u pper right of the red cir cle”
  23. 23. The Model• Training the model – The Lexical Analysis Unit • Discovers string-meaning associations – The Syntactic Analysis Unit • Discovers compositional relationships
  24. 24. The Lexical Analysis Unit<event, description> pairs are compared through a form of cross-situational analysis <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  25. 25. The Lexical Analysis UnitFeature tuple comparisons are value sensitive and object identifier insensitive. Two feature tuples, <v1, (o1)> and <v2, (o2)>, are equivalent iff v1 = v2 <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  26. 26. The Lexical Analysis UnitCo-occurring syllable sequences are found <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”
  27. 27. The Lexical Analysis Unit New <feature tuple set, description> pairs are derived <event, description>#1 {<red, (1)>, <circle, (1)>, <pink, (2)>, <cross, (2)>, <above, (0)>, <right, (0)>} “a pink cross to the u pper right of the red cir cle” <event, description>#2 {<green, (1)>, <circle, (1)>, <red, (2)>, <diamond, (2)>, <even_vertical, (0)>, <right, (0)>} “a red dia mond to the right of the green cir cle”<{<red, (1)>, <circle, (1)>, <right, (0)>}, “a”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “a”><{<red, (1)>, <circle, (1)>, <right, (0)>}, “to the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “red”><{<red, (1)>, <circle, (1)>, <right, (0)>}, “right of <{<circle, (1)>, <red, (2)>, <right, (0)>}, “to the”>the”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “right of<{<red, (1)>, <circle, (1)>, <right, (0)>}, “red”> the”><{<red, (1)>, <circle, (1)>, <right, (0)>}, “cir cle”> <{<circle, (1)>, <red, (2)>, <right, (0)>}, “cir cle”>
  28. 28. The Lexical Analysis Unit• Cross-situational analysis can produce pairs that share the same strings (homonyms) or the same feature sets (synonyms)• Homonyms and synonyms are removed, following a principle of mutual exclusivity (Markman and Wachtel, 1988)• When pairs are equal, with insensitivity to object identifiers, they are merged. Merging produces a new pair, that expresses both of the relationships <{<red, (1)>}, “red”> is merged with <{<red, (2)>}, “red”> to produce <{<red, (1, 2)>}, “red”>
  29. 29. The Lexical Analysis Unit• From all merged pairs, homonyms are removed by selecting the most probable feature set for each string, Frequency of (Sj | Fi ) P(Fi | Sj ) = Frequency of Sj where Frequency of (Sj | Fi) is the number of times that Sj has been observed with Fi and the Frequency of Sj is the number of times that Sj has been observed• Then synonyms are removed by selecting the most probable string for each feature set, P(Sj | Fi), and erasing the remaining pair’s feature sets• A set of lexical items are derived
  30. 30. The Syntactic Analysis Unit• Compositional relationships are found by combining and comparing lexical items• Lexical items are combined by set union and string concatenation f 1, s1 combined with f 2, s 2 = f 1  f 2, s1 + s 2• The lexical item triple <<f1, s1>, <f2, s2>, <f3, s3>> expresses a compositional relationship iff <f1, s1> = <f2, s2> combined with <f3, s3>
  31. 31. The Syntactic Analysis UnitA lexical item triple can be made to express a rule by:1. Converting lexical items into phrasal categories2. Constructing transformations
  32. 32. The Syntactic Analysis UnitA lexical item triple can be made to express a rule by:1. Converting lexical items into phrasal categories2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>>
  33. 33. The Syntactic Analysis UnitA lexical item triple can be made to express a rule by:1. Converting lexical items into phrasal categories2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
  34. 34. The Syntactic Analysis UnitA lexical item triple can be made to express a rule by:1. Converting lexical items into phrasal categories2. Constructing transformations <<{<red, (1, 2)>, <square, (1, 2)>}, “red square”>, <{<red, (1, 2)>}, “red”>, <{<square, (1, 2)>}, “square”>> <{<red, (1, 2)>, <square, (1, 2)>}, “red square”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<square, (1, 2)>}, “square”>
  35. 35. The Syntactic Analysis UnitRules that modify object identifiers can be constructed <{<red, (2)>}, “a red”> () (<(1, 2) → (2)>) <{}, “a”> <{<red, (1, 2)>}, “red”> <{<blue, (1)>}, “the blue”> () (<(1, 2) → (1)>) <{}, “the”> <{<blue, (1, 2)>}, “blue”>
  36. 36. The Syntactic Analysis UnitRules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>)<{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
  37. 37. The Syntactic Analysis UnitRules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>)<{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”>
  38. 38. The Syntactic Analysis Unit Rules can be merged when they share transformations <{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”> <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<red, (1, 2)>}, “red”> <{<circle, (1, 2)>}, “cir cle”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<blue, (1, 2)>}, “blue”> <{<circle, (1, 2)>}, “cir cle”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”> (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”> <{<diamond, (1, 2)>}, “dia mond”> <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>){<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  39. 39. Comprehension• The model is tested for evidence of language acquisition through comprehension tasks• The model can comprehend a string by: – Finding it in a phrasal category (lexical) – Or creating it through applying a rule (syntactic)
  40. 40. Comprehension• Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  41. 41. Comprehension• Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is {<circle, (1, 2)>}
  42. 42. Comprehension• Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  43. 43. Comprehension• Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule • Find “cir” and “cle” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  44. 44. Comprehension• Example 1. Comprehension of “cir cle” – Find “cir cle” in a phrasal category – Attempt to create “cir cle” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found lexically as {<circle, (1, 2)>}
  45. 45. Comprehension• Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  46. 46. Comprehension• Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  47. 47. Comprehension• Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}
  48. 48. Comprehension • Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}{<red, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), and combined with{<diamond, (1, 2)>}, transformed by (<(1, 2)> → (1, 2)>), gives {<red, (1, 2)>, <diamond, (1, 2)>}
  49. 49. Comprehension• Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule • Find “red” and “dia mond” in phrasal categories of a rule • Find “red dia” and “mond” in phrasal categories of a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} No meaning found
  50. 50. Comprehension• Example 2. Comprehension of “red dia mond” – Find “red dia mond” in a phrasal category – Attempt to create “red dia mond” by applying a rule {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>} Meaning is found syntactically as {<red, (1, 2)>, <diamond, (1, 2)>}
  51. 51. Comprehension • Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} {<{<pink, (1)>}, “the pink”>, <{<green, (1, 2)>}, “green”>, <{<green, (1)>}, “the green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) <{<white, (1)>}, “the white”>} () {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, (<(1, 2) → (1)>) <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>}{<{}, “the”>} {<{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
  52. 52. Comprehension• Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} Construction Islands <{<pink, (1, 2)>}, “pink”>}
  53. 53. Comprehension• Phrasal categories are substitutable for one another if they share a subset relationship {<{<red, (2)>}, “a red”>, <{<blue, (2)>}, “a blue”>, <{<pink, (2)>}, “a pink”>, <{<green, (2)>}, “a green”>, <{<white, (2)>}, “a white”>} () (<(1, 2) → (2)>) {<{}, “a”>} {<{<red, (1, 2)>}, “red”>, {<{<red, (1, 2)>, <circle, (1, 2)>}, “red cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<blue, (1, 2)>, <circle, (1, 2)>}, “blue cir cle”>, <{<pink, (1, 2)>}, “pink”>, <{<pink, (1, 2)>, <diamond, (1, 2)>}, “pink dia mond”>} <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>} (<(1, 2) → (1, 2)>) (<(1, 2) → (1, 2)>) {<{<red, (1, 2)>}, “red”>, {<{<circle, (1, 2)>}, “cir cle”>, <{<blue, (1, 2)>}, “blue”>, <{<diamond, (1, 2)>}, “dia mond”>} <{<pink, (1, 2)>}, “pink”>, <{<green, (1, 2)>}, “green”>, <{<white, (1, 2)>}, “white”>}
  54. 54. Results• Observing the developmental shift from lexical to syntactic comprehension – Tested for comprehension of colours (10), shapes (10), and colour shape combinations (100) during training. Results are averaged over 10 sessions. Developmental Shift 25 3 3 3 3 8 Pre-linguistic Holophrastic 20 Multi-word Early comprehended No. strings 15 Lexical 10 Syntactic 5 0 0 3 6 9 12 15 18 21 24 27 30 No. <event, description>s entered
  55. 55. Results• Comprehension of colours and shapes compared to colour shape combinations – Tested for lexical comprehension of colours (10), and shapes (10), and syntactic comprehension of colour shape combinations (100) during training. Results are averaged over 10 sessions. Expressivity f 100 % string set 80 comprehended 60 Lexical 40 Syntactic 20 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 No. <event, description>s entered
  56. 56. ConclusionsThe model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derived
  57. 57. ConclusionsThe model demonstrates staged linguistic acquisition – No maturational triggers are employed – Training data are kept constant – Lexical items are required before compositions can be derivedCan this work be extended into further stage transitions?
  58. 58. Thank you
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×