1. Models of Grammar Learning CS 182 Lecture April 26, 2007

2. 2 What constitutes learning a language? What are the sounds (Phonology) How to make words (Morphology) What do words mean (Semantics) How to put words together (Syntax) Social use of language (Pragmatics) Rules of conversations (Pragmatics)

3. 3 Language Learning Problem Prior knowledge Initial grammar G (set of ECG constructions) Ontology (category relations) Language comprehension model (analysis/resolution) Hypothesis space: new ECG grammar G’ Search = processes for proposing new constructions Relational Mapping, Merge, Compose

4. 4 Language Learning Problem Performance measure Goal: Comprehension should improve with training Criterion: need some objective function to guide learning… Minimum Description Length: Probability of Model given Data:

5. 5 Minimum Description Length Choose grammar G to minimize cost(G|D): cost(G|D) = α size(G) + β complexity(D|G) Approximates Bayesian learning; cost(G|D) ≈ posterior probability P(G|D) Size of grammar = size(G) ≈ prior P(G) favor fewer/smaller constructions/roles; isomorphic mappings Complexity of data given grammar ≈ likelihood P(D|G) favor simpler analyses (fewer, more likely constructions) based on derivation length + score of derivation

6. 6 Size Of Grammar Size of the grammar G is the sum of the size of each construction: Size of each construction c is: where n c = number of constituents in c, m c = number of constraints in c, length(e) = slot chain length of element reference e

7. 7 What do we know about language development? (focusing mainly on first language acquisition of English-speaking, normal population)

8. 8 Children are amazing learners cooing reduplicated babbling first word 0 mos2 yr6 mos3 yrs4 yrs5 yrs12 mos two-word combinationsmulti-word utterances questions, complex sentence structures, conversational principles

9. 9 Phonology: Non-native contrasts Werker and Tees (1984) Thompson: velar vs. uvular, /`ki/-/`qi/. Hindi: retroflex vs. dental, /t.a/-/ta/

10. 10 Finding words: Statistical learning Saffran, Aslin and Newport (1996) /bidaku/, /padoti/, /golabu/ /bidakupadotigolabubidaku/ 2 minutes of this continuous speech stream By 8 months infants detect the words (vs non-words and part-words) pretty baby

11. 11 Word order: agent and patient Hirsch-Pasek and Golinkoff (1996) 1;4-1;7 mostly still in the one-word stage Where is CM tickling BB?

12. 12 Early syntax agent + action‘Daddy sit’ action + object‘drive car’ agent + object‘Mommy sock’ action + location‘sit chair’ entity + location‘toy floor’ possessor + possessed‘my teddy’ entity + attribute‘crayon big’ demonstrative + entity‘this telephone’

13. 13 MOTHER:what are you doing? NAOMI:I climbing up. MOTHER:you’re climbing up? 2;0.18 FATHER:what’s the boy doing to the dog? NAOMI:squeezing his neck. NAOMI:and the dog climbed up the tree. NAOMI:now they’re both safe. NAOMI:but he can climb trees. 4;9.3 FATHER:Nomi are you climbing up the books? NAOMI:up. NAOMI:climbing. NAOMI:books. 1;11.3 Sachs corpus (CHILDES) From Single Words To Complex Utterances

14. 14 How Can Children Be So Good At Learning Language? Gold’s Theorem: No superfinite class of language is identifiable in the limit from positive data only Principles & Parameters Babies are born as blank slates but acquire language quickly (with noisy input and little correction) → Language must be innate: Universal Grammar + parameter setting But babies aren’t born as blank slates! And they do not learn language in a vacuum!

15. 15 Modifications of Gold’s Result (Weakly) Ordered Examples, implicit negatives Loosened Identification Conditions Complexity Measures, Best Fit No Theorems will resolve these issues

16. 16 Modeling the acquisition of grammar: Theoretical assumptions

17. 17 Language Acquisition Opulence of the substrate Prelinguistic children already have rich sensorimotor representations and sophisticated social knowledge intention inference, reference resolution language-specific event conceptualizations (Bloom 2000, Tomasello 1995, Bowerman & Choi, Slobin, et al.) Children are sensitive to statistical information Phonological transitional probabilities Even dependencies between non-adjacent items (Saffran et al. 1996, Gomez 2002)

18. 18 Language Acquisition Basic Scenes Simple clause constructions are associated directly with scenes basic to human experience (Goldberg 1995, Slobin 1985) Verb Island Hypothesis Children learn their earliest constructions (arguments, syntactic marking) on a verb-specific basis (Tomasello 1992) get ball get bottle get OBJECT … throw frisbee throw ball throw OBJECT … this should be reminiscent of your model merging assignment

19. 19 Comprehension is partial. (not just for dogs)

20. 20 What children pick up from what they hear Children use rich situational context / cues to fill in the gaps They also have at their disposal embodied knowledge and statistical correlations (i.e. experience) what did you throw it into? they’re throwing this in here. they’re throwing a ball. don’t throw it Nomi. well you really shouldn’t throw things Nomi you know. remember how we told you you shouldn’t throw things. what did you throw it into? they’re throwing this in here. they’re throwing a ball. don’t throw it Nomi. well you really shouldn’t throw things Nomi you know. remember how we told you you shouldn’t throw things.

21. 21 Language Learning Hypothesis Children learn constructions that bridge the gap between what they know from language and what they know from the rest of cognition

22. 22 Modeling the acquisition of (early) grammar: Comprehension-driven, usage-based

23. Natural Language Processing at Berkeley Dan Klein EECS Department UC Berkeley

24. 24 NLP: Motivation It’d be great if machines could Read text and understand it Translate languages accurately Help us manage, summarize, and aggregate information Use speech as a UI Talk to us / listen to us But they can’t Language is complex Language is ambiguous Language is highly structured

25. 25 Machine Translation Syntactic MT Learn grammar mappings between languages Fully data-driven

26. 26 Information Extraction Unsupervised Coreference Resolution Take in lots of text Learn what the entities are and how they corefer Fully unsupervised, but gets supervised performance! General research goal: unsupervised learning of meaning

27. 27 Syntactic Learning Grammar Induction Raw text in Learned grammars out Big result: this can be done! Grammar Refinement Coarse grammars in Detailed grammars out Gives top parsing systems

28. 28 Influental members of the House Ways and Means Committee introduced legislation that would restrict how the new S&L bailout agency can raise capital, creating another potential obstacle to the government's sale of sick thrifts. Syntactic Inference Natural language is very ambiguous Grammars are huge Billions of parses to consider Milliseconds to do it

29. 29 Non-Parametric Grammars

30. 30 Idea: Learn PCFGs with EM Classic experiments on learning PCFGs with Expectation- Maximization [Lari and Young, 1990] Full binary grammar over n symbols Parse uniformly/randomly at first Re-estimate rule expectations off of parses Repeat Their conclusion: it doesn’t really work. XjXj XiXi XkXk { X 1, X 2 … X n }

31. 31 Re-estimation of PCFGs Basic quantity needed for re-estimation with EM: Can calculate in cubic time with the Inside-Outside algorithm. Consider an initial grammar where all productions have equal weight: Then all trees have equal probability initially. Therefore, after one round of EM, the posterior over trees will (in the absence of random perturbation) be approximately uniform over all trees, and symmetric over symbols.

32. 32 Problem: “Uniform” Posteriors Tree Uniform Split Uniform

33. 33 Problem: Model Symmetries Symmetries How does this relate to trees? NOUN VERB ADJ NOUN X1?X1?X2?X2?X1?X1?X2?X2? NOUN VERB NOUN VERB ADJ

34. 34 Overview: NLP at UCB Lots of research and resources: Dan Klein: Statistical NLP / ML Marti Hearst: Stat NLP / HCI Jerry Feldman: Language and Mind Michael Jordan: Statistical Methods / ML Tom Griffiths: Statistical Learning / Psychology ICSI Speech and AI groups (Morgan, Stolcke, Shriberg, Narayanan…) Great linguistics and stats departments! No better place to solve the hard NLP problems!

35. 35 Other Approaches Evaluation: fraction of nodes in gold trees correctly posited in proposed trees (unlabeled recall) Some recent work in learning constituency: [Adrians, 99] Language grammars aren’t general PCFGs [Clark, 01] Mutual-information filters detect constituents, then an MDL-guided search assembles them [van Zaanen, 00] Finds low edit-distance sentence pairs and extracts their differences Adriaans, 199916.8 Clark, 200134.6 van Zaanen, 200035.6

36. 36

37. 37 Embodied Construction Grammar (Bergen and Chang 2005) construction T HROWER -T HROW -O BJECT constructional constituents t1 : R EF- E XPRESSION t2 : T HROW t3 : O BJECT -R EF form t1 f before t2 f t2 f before t3 f meaning t2 m.thrower ↔ t1 m t2 m.throwee ↔ t3 m role-filler bindings

38. 38 “you” you schema Addressee subcase of Human FORM (sound) MEANING (stuff) Analyzing “You Throw The Ball” “throw” throw schema Throw roles: thrower throwee “ball” ball schema Ball subcase of Object “block” block schema Block subcase of Object t1 before t2 t2 before t3 Thrower- Throw-Object t2.thrower ↔ t1 t2.throwee ↔ t3 “the” Addressee Throw thrower throwee Ball

39. 39 Constructions (Utterance, Situation) 1.Learner passes input (Utterance + Situation) and current grammar to Analyzer. Analyze Semantic Specification, Constructional Analysis 2.Analyzer produces SemSpec and Constructional Analysis. 3.Learner updates grammar: Hypothesize a.Hypothesize new map. Reorganize b.Reorganize grammar (merge or compose). c.Reinforce (based on usage). Learning-Analysis Cycle (Chang, 2004)

40. 40 Hypothesizing a new construction through relational mapping

41. 41 “you” “throw” “ball” you throw ball “block” block schema Addressee subcase of Human FORM (sound) MEANING (stuff) lexical constructions Initial Single-Word Stage schema Throw roles: thrower throwee schema Ball subcase of Object schema Block subcase of Object

42. 42 “you” you schema Addressee subcase of Human FORMMEANING New Data: “You Throw The Ball” “throw” throw schema Throw roles: thrower throwee “ball” ball schema Ball subcase of Object “block” block schema Block subcase of Object “the” Addressee Throw thrower throwee Ball Self SITUATION Addressee Throw thrower throwee Ball before role-filler throw-ball

43. 43 New Construction Hypothesized construction THROW-BALL constructional constituents t : THROW b : BALL form t f before b f meaning t m.throwee ↔ b m

44. 44 Three kinds of meaning relations 1. When B.m fills a role of A.m 2. When A.m and B.m are both filled by X 3. When A.m and B.m both fill roles of X throw ball throw.throwee ↔ ball put ball down put.mover ↔ ball down.tr ↔ ball Nomi ball possession.possessor ↔ Nomi possession.possessed ↔ ball

45. 45 Reorganizing the current grammar through merge and compose

46. 46 Merging Similar Constructions throw before block Throw.throwee = Block throw before ball Throw.throwee = Ball throw before-s ing Throw.aspect = ongoing throw-ing the ball throw the block throw before Object f THROW.throwee = Object m THROW- OBJECT

47. 47 Resulting Construction construction THROW-OBJECT constructional constituents t : THROW o : OBJECT form t f before o f meaning t m.throwee ↔ o m

48. 48 Composing Co-occurring Constructions ball before off Motion m m.mover = Ball m.path = Off ball off throw before ball Throw.throwee = Ball throw the ball throw before ball ball before off THROW.throwee = Ball Motion m m.mover = Ball m.path = Off THROW- BALL- OFF

49. 49 Resulting Construction construction THROW-BALL-OFF constructional constituents t : THROW b : BALL o : OFF form t f before b f b f before o f meaning evokes MOTION as m t m.throwee ↔ b m m.mover ↔ b m m.path ↔ o m

50. 50 Precisely defining the learning algorithm

51. 51 Example: The Throw-Ball Cxn construction THROW-BALL constructional constituents t : THROW b : BALL form t f before b f meaning t m.throwee ↔ b m size ( THROW-BALL ) = 2 + 2 + (2 + 3) = 9

52. 52 Language Learning Problem Performance measure Goal: Comprehension should improve with training Criterion: need some objective function to guide learning… Minimum Description Length: Probability of Model given Data:

53. 53 Minimum Description Length Choose grammar G to minimize cost(G|D): cost(G|D) = α size(G) + β complexity(D|G) Approximates Bayesian learning; cost(G|D) ≈ posterior probability P(G|D) Size of grammar = size(G) ≈ prior P(G) favor fewer/smaller constructions/roles; isomorphic mappings Complexity of data given grammar ≈ likelihood P(D|G) favor simpler analyses (fewer, more likely constructions) based on derivation length + score of derivation

54. 54 Size Of Grammar Size of the grammar G is the sum of the size of each construction: Size of each construction c is: where n c = number of constituents in c, m c = number of constraints in c, length(e) = slot chain length of element reference e

55. 55 Complexity of Data Given Grammar Complexity of the data D given grammar G is the sum of the analysis score of each input token d: Analysis score of each input token d is: where c is a construction used in the analysis of d weight c ≈ relative frequency of c, |type r | = number of ontology items of type r used, height d = height of the derivation graph, semfit d = semantic fit provide by the analyzer

56. 56 Preliminary Results

57. 57 Experiment: Learning Verb Islands Subset of the CHILDES database of parent-child interactions (MacWhinney 1991; Slobin et al.) coded by developmental psychologists for form: particles, deictics, pronouns, locative phrases, etc. meaning: temporality, person, pragmatic function, type of motion (self-movement vs. caused movement; animate being vs. inanimate object, etc.) crosslinguistic (English, French, Italian, Spanish) English motion utterances: 829 parent, 690 child utterances English all utterances: 3160 adult, 5408 child age span is 1;2 to 2;6

58. 58 Learning Throw-Constructions 1. Don’t throw the bear.throw-bear 2. you throw ityou-throw 3. throwing the thing.throw-thing 4. Don’t throw them on the ground.throw-them 5. throwing the frisbee.throw-frisbee MERGEthrow-OBJ 6. Do you throw the frisbee? COMPOSE you-throw-frisbee 7. She’s throwing the frisbee. COMPOSE she-throw-frisbee

59. 59 Learning Results

60. 60 Summary Cognitively plausible situated learning processes What do kids start with? perceptual, motor, social, world knowledge meanings of single words What kind of input drives acquisition? Social-pragmatic knowledge Statistical properties of linguistic input What is the learning loop? Use existing linguistic knowledge to analyze input Use social-pragmatic knowledge to understand situation Hypothesize new constructions to bridge the gap

61. 61 2H 2 O + 2SO 2 + O 2 → 2H 2 SO 4H O SO In the gas phase sulfur dioxide is oxidized by reaction with the hydroxyl radical via a termolecular reaction:gas phasehydroxyltermolecular SO2 OH· → HOSO2· which is followed by: HOSO2· + O2 → HO2· + SO3 In the presence of water sulfur trioxide (SO3) is converted rapidly to sulfuric acid:sulfur trioxidesulfuric acid SO3(g) + H2O(l) → H2SO4(l)