Compositional Semantic Parsing on Semi-Structured Tables

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1 Compositional Semantic Parsing on Semi-Structured Tables Ice Pasupat and Percy Liang Stanford University ACL 2015 Tuesday, July 28, 2015

2 Task Question answering given a knowledge source In which city was Ada Lovelace born? Database 2

3 Semantic Parsing Parse questions into executable logical forms In which city was Ada Lovelace born? Type.City PeopleBornHere.AdaLovelace (Lambda DCS) Database 3

4 Semantic Parsing Logical forms can be executed on the knowledge source to get denotations Type.City PeopleBornHere.AdaLovelace City Database 4

5 Semantic Parsing Logical forms can be executed on the knowledge source to get denotations Type.City PeopleBornHere.AdaLovelace Shanghai Type City London Type Database Capital England 5

6 Semantic Parsing Logical forms can be executed on the knowledge source to get denotations Type.City PeopleBornHere.AdaLovelace Shanghai Type City London Type Database Capital England 6

7 Semantic Parsing Logical forms can be executed on the knowledge source to get denotations Type.City PeopleBornHere.AdaLovelace Shanghai London Database... 7

8 Compositionality We can compose logical forms into bigger ones with logical operations Type.City PeopleBornHere.AdaLovelace England Shanghai London... London... 8

9 Compositionality We can compose logical forms into bigger ones with logical operations Intersection Type.City PeopleBornHere.AdaLovelace England Shanghai London... London... 9

10 Compositionality We can compose logical forms into bigger ones with logical operations Intersection Type.City PeopleBornHere.AdaLovelace London 10

11 Compositionality We can compose logical forms into bigger ones with logical operations Type.City Type.State cities and / or states count(type.city) how many cities argmax(type.city, Area) largest city sum(areaof.type.city) total area of all cities AreaOf.London AreaOf.Paris how much bigger is London than Paris? 11

12 Related Work Early systems: Parse very compositional questions into database queries How many rivers are in the state with the largest population? answer(a, count(b, (river(b), loc(b, C), largest(d, (state(c), population(c, D)))), A))) Compositionality: High [Zelle + Mooney, 1996 / Wong + Mooney, 2007 / Zettlemoyer + Collins, 2007 / Kwiatkowski et al., 2011 /...] 12

13 Related Work Early systems: Parse very compositional questions into database queries How many rivers are in the state with the largest population? answer(a, count(b, (river(b), loc(b, C), largest(d, (state(c), population(c, D)))), A))) Compositionality: High Geography Knowledge source: Database few entities / relations fixed schema [Zelle + Mooney, 1996 / Wong + Mooney, 2007 / Zettlemoyer + Collins, 2007 / Kwiatkowski et al., 2011 /...] 13

14 Related Work Depth (compositionality) Early Systems Breadth (domain size) 14

15 Related Work Scaling to large knowledge bases (KBs): Answer open-domain questions using curated KBs In which comic book issue did Kitty Pryde first appear? NELL Knowledge source: Large KBs lots of entities / relations fixed schema [Cai + Yates, 2013 / Berant et al., / Fader et al., 2014 / Reddy et al., 2014 /...] 15

16 Related Work Scaling to large knowledge bases (KBs): Answer open-domain questions using curated KBs In which comic book issue did Kitty Pryde first appear? R[FirstAppearance].KittyPryde NELL Compositionality: Lower Knowledge source: Large KBs lots of entities / relations fixed schema [Cai + Yates, 2013 / Berant et al., / Fader et al., 2014 / Reddy et al., 2014 /...] 16

17 Related Work Scaling to large knowledge bases (KBs): Answer open-domain questions using curated KBs In which comic book issue did Kitty Pryde first appear? R[FirstAppearance].KittyPryde Compositionality: Lower Still, only < 10% of general questions can be answered by Freebase [Berant etnell al., 2013] Knowledge source: Large KBs lots of entities / relations fixed schema [Cai + Yates, 2013 / Berant et al., / Fader et al., 2014 / Reddy et al., 2014 /...] 17

18 Related Work Depth (compositionality) Early Systems Scale to KBs Breadth (domain size) 18

19 Related Work Web search: Keyword search over the whole Web (information retrieval / not semantic parsing) stanford cs professors Compositionality: None Knowledge source: Internet open-domain unstructured (no schema) 19

20 Related Work Depth (compositionality) Early Systems Scale to KBs Web Search Breadth (domain size) 20

21 Motivation Web text in general is too unstructured However, the Web also contains semi-structured data (tables, lists, repeated headings,...) 21

22 stanford cs professors 22

23 Motivation Web text in general is too unstructured However, the Web also contains semi-structured data (tables, lists, repeated headings,...) Open-domain: lots of information with arbitrary data schema [Cafarella et al., 2008 (WebTables)] 23

24 Motivation Web text in general is too unstructured However, the Web also contains semi-structured data (tables, lists, repeated headings,...) Open-domain: lots of information with arbitrary data schema [Cafarella et al., 2008 (WebTables)] Structured enough to allow complex logical operations (~ mini knowledge base) How many Stanford CS professors do not have offices in the Gates building? 24

25 Motivation Web text in general is too unstructured However, the Web also contains semi-structured data (tables, lists, repeated headings,...) Open-domain: lots of information with arbitrary data schema [Cafarella et al., 2008 (WebTables)] Structured enough to allow complex logical operations (~ mini knowledge base) Task: Answer compositional questions based on semi-structured tables from the Web 25

26 Motivation Depth (compositionality) Semantic Parsing on Semi-Structured Data Early Systems Scale to KBs Web Search Breadth (domain size) 26

27 Outline Background and Related Work Task and Dataset Approach Experiments 27

28 Task Description Input: utterance x and HTML table t Output: answer y Year City Country Nations 1896 Athens Greece Paris France St. Louis USA Athens Greece Beijing China London UK 204 x = Greece held its last Summer Olympics in which year? y =

29 Task Description Input: utterance x and HTML table t Output: answer y Training data: list of (x, t, y) no logical form Tables in test data are not seen during training The model must generalize to unseen table schemas! 29

30 Dataset WikiTableQuestions dataset: Tables t are from Wikipedia 30

31 31

32 Dataset WikiTableQuestions dataset: Tables t are from Wikipedia Questions x and answers y are from Mechanical Turk Prompts are given to encourage compositionality How many est last above same as difference or his Requires counting etc. 32

33 Dataset WikiTableQuestions dataset: Tables t are from Wikipedia Questions x and answers y are from Mechanical Turk Prompts are given to encourage compositionality Prompt: The question must contains "last" (or a synonym) In what city did Piotr's last 1st place finish occur? 33

34 In what city did Piotr's last 1st place finish occur? 34

35 In what city did Piotr's last 1st place finish occur? 35

36 In what city did Piotr's last 1st place finish occur? 36

37 In what city did Piotr's last 1st place finish occur? 37

38 How long did it take this competitor to finish the 4x400 meter relay at Universiade in 2005? 38

39 Where was the competition held immediately before the one in Turkey? 39

40 How many times has this competitor placed 5th or better in competition? 40

41 Dataset WikiTableQuestions dataset: 2100 tables Average: 6.3 columns / 27.5 rows examples 41

42 Challenges With increased breadth (semi-structured data): Must generalize to arbitrary table schemas (as opposed to the fixed database schema) Test tables are unseen Cannot precompute a lexicon mapping phrases to table relations Table headers (Year, Competition, Venue, ) 42

43 Challenges With increased breadth (semi-structured data): Must generalize to arbitrary table schemas (as opposed to the fixed database schema) Test tables are unseen Cannot precompute a lexicon mapping phrases to table relations With increased depth (compositional questions): More operations and deeper recursion Number of possible parses grows exponentially 43

44 Outline Background and Related Work Task and Dataset Approach Experiments 44

45 Approach Greece held its last Summer Olympics in which year? x t y

46 Approach Greece held its last Summer Olympics in which year? R[λx[Year.Date.x]]. argmax(..., Index) x z t (3) Execution y

47 Approach Greece held its last Summer Olympics in which year? x t (1) Generation Set of candidates R[λx[Year.Date.x]]. argmax(..., Index) Z z (3) Execution y

48 Approach Greece held its last Summer Olympics in which year? x t (1) Generation Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

49 Approach Greece held its last Summer Olympics in which year? x t (1) Generation Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

50 Representation Convert table t to knowledge graph w Year City Country Nations 1896 Athens Greece Paris France St. Louis USA Athens Greece Beijing China London UK 204 Year 1896 City Athens Year 1900 City Paris 50

51 Representation Convert table t to knowledge graph w Year City Country Nations 1896 Athens Greece Paris France St. Louis USA Athens Greece Beijing China London UK 204 Index 0 Year Next 1896 City Athens Year Next 1900 Number City Paris Date 1900-XX-XX 51

52 Approach Greece held its last Summer Olympics in which year? x t (1) Generation Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

53 Approach 0 Index Greece held its last Summer Olympics in which year? Year Next x 1896 w City Athens Year Next 1900 City Paris Number (1) Generation Date 1900-XX-XX Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

54 Approach 0 Index Greece held its last Summer Olympics in which year? Year Next x 1896 w City Athens Year Next 1900 City Paris Number (1) Generation Date 1900-XX-XX Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

55 Generation Build formulas bottom-up according to a set of deduction rules R[λx[Year.Date.x]].argmax(Country.Greece, Index) Greece held its last Summer Olympics in which year? 55

56 Generation Build formulas bottom-up according to a set of deduction rules Index 0 Year Next 1896 City Athens Country Greece Greece held its last Summer Olympics in which year? Greece 56

57 Generation Build formulas bottom-up according to a set of deduction rules "anchored" to the word Greece Greece TokenSpan Entity Greece held its last Summer Olympics in which year? Greece 57

58 Generation Complication: Some logical predicates (e.g., relation Country) don't map to any phrase R[λx[Year.Date.x]].argmax(Country.Greece, Index) Greece Greece held its last Summer Olympics in which year? Greece 58

59 Generation Complication: Some logical predicates (e.g., relation Country) don't map to any phrase R[λx[Year.Date.x]].argmax(Country.Greece, Index) Greece Even when there is such a phrase, we may still don't know the mapping if we have not seen the relation in any table in the training data Greece held its last Summer Olympics in which year? Greece 59

60 Generation Idea: Allow formulas to be created from nothing ("floating") Inspired by "bridging" [Berant et al., 2013] Greece Greece held its last Summer Olympics in which year? Greece 60

61 Generation Idea: Allow formulas to be created from nothing ("floating") Inspired by "bridging" [Berant et al., 2013] The relation Country is "floating" Greece Country ø Relation Greece held its last Summer Olympics in which year? Greece 61

62 Generation Idea: Allow formulas to be created from nothing ("floating") Inspired by "bridging" [Berant et al., 2013] Country.Greece Greece Country List of all rows with country Greece Relation + Values Records [Relation.Values] Greece held its last Summer Olympics in which year? Greece 62

63 Generation Entities are anchored to token spans Relations and Operations are not argmax(country.greece, Index) Country.Greece Greece Index The last row with country Greece Country Greece held its last Summer Olympics in which year? Greece 63

64 Generation Connection between floating predicates and phrases in the question are made during ranking argmax(country.greece, Index) Country.Greece Greece Index The last row with country Greece Country Greece held its last Summer Olympics in which year? Greece 64

65 Generation Problem: Over-generation due to high recursion Redundant argmin argmin(argmax(country.greece, Index), Index) argmax(country.greece, Index) Country.Greece Index Index Handled by beam search and pruning heuristics 65

66 Approach 0 Index Greece held its last Summer Olympics in which year? Year Next x 1896 w City Athens Year Next 1900 City Paris Number (1) Generation Date 1900-XX-XX Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

67 Approach 0 Index Greece held its last Summer Olympics in which year? Year Next x 1896 w City Athens Year Next 1900 City Paris Number (1) Generation Date 1900-XX-XX Z (2) Ranking R[λx[Year.Date.x]]. argmax(..., Index) z (3) Execution y

68 Ranking Given a set Z of candidate formulas z, define a loglinear distribution: pθ(z x, w) exp {θtφ(x, w, z)} where θ = parameter vector φ(x, w, z) = feature vector 68

69 Ranking Features: Relate phrases in x to predicates in z (phrase = last, predicate = argmax) (phrase = year, predicate = Year) phrase == predicate 69

70 Ranking Features: Relate phrases in x to predicates in z (phrase = last, predicate = argmax) (phrase = year, predicate = Year) phrase == predicate Relate phrases in x to properties of y = z w (headword = year, answer's type = NUMBER) headword == answer's column 70

71 Learning Given training example (x, w, y), define pθ(y x, w) = Σ p (z x, w) I(y = z ) θ w z Z As usual, we choose θ to maximize the (L1 regularized) expectation of log pθ(y x, w) over training data 71

72 Outline Background and Related Work Task and Dataset Approach Experiments 72

73 Results Oracle: Able to generate a candidate formula z Z that executes to y Accuracy: The highest-ranked z executes to y 73

74 Results Oracle: Able to generate a candidate formula z Z that executes to y Accuracy: The highest-ranked z executes to y Two baselines: IR-inspired: Pick an answer among table cells by putting softmax over table cells WQ: Restrict the generation rules to the ones from Berant and Liang (2014) 74

75 Results on Test Set IR-inspired accuracy oracle WQ This work In all settings, tables in test data are not seen during training 75

76 Results on Test Set accuracy oracle IR-inspired WQ This work In all settings, tables in test data are not seen during training 76

77 Results on Test Set accuracy oracle IR-inspired WQ This work In all settings, tables in test data are not seen during training 77

78 Positive Examples What is the last title that spicy horse produced? How many districts have a population density of at least 1000? Who finished directly after the driver who finished in 1:28.745? (Information retrieval alone can't answer these questions) 78

79 Error Analysis (1) Anchoring [18%] how many mexican swimmers ranked in the top 10? Rank Swimmer Country Time Note Mexico 79

80 Error Analysis (2) Normalization [29%] how long does the show defcon 3 last? ET Days Program Hosts Description 2pm 3pm 80

81 Error Analysis (3) Unhandled Operations [19%] was there more gold medals won than silver? (boolean answer) which movies were number 1 for at least two consecutive weeks? (consecutive count) how many titles had the same author listed as the illustrator? (count rows with arbitrary conditions) 81

82 Error Analysis (4) Ranking Errors [24%] how many buildings on the list are taller than 200 feet? Name Street Address Years as Tallest Height ft (m) Floors 792 (241) 82

83 Conclusion Depth (compositionality) Semantic Parsing on Semi-Structured Data Early Systems Scale to KBs Web Search Breadth (domain size) 83

84 Conclusion Dataset and reproducible experiments are available on CodaLab: nlp.stanford.edu/software/sempre/wikitable Thank you 84

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