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Agreement matters: Challenges of translating into a MRL Yoav Goldberg Ben Gurion University.

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Presentation on theme: "Agreement matters: Challenges of translating into a MRL Yoav Goldberg Ben Gurion University."— Presentation transcript:

1 Agreement matters: Challenges of translating into a MRL Yoav Goldberg Ben Gurion University

2 Why should we care about syntactic modeling of MRLs?

3 A brief summary

4 What Kevin said.

5 Example: English  Hebrew I wash the car

6 Example: English  Hebrew אני רוחץ את המכונית I wash the car (I wash the car)

7 Example: English  Hebrew I wash the floor אני רוחץ את המכונית I wash the car (I wash the car)

8 Example: English  Hebrew I wash the floor אני רוחץ את המכונית I wash the car (I wash the car) אני שוטפת את הרצפה (I wash the floor)

9 Example: English  Hebrew I wash the floor אני רוחץ את המכוניתאני שוטפת את הרצפה I wash the car (I wash the floor) (I wash the car)

10 Example: English  Hebrew I wash the floor אני רוחץ את המכוניתאני שוטפת את הרצפה I wash the car (I wash the floor) (I wash the car) FeminineMasculine

11 Hebrew Verbs are morphologically marked for Gender (and Number, and Person, and Tense..) Hebrew Fact 1

12 Example: English  Hebrew I wash the floor אני רוחץ את המכוניתאני שוטפת את הרצפה I wash the car (I wash the floor) (I wash the car) FeminineMasculine Are these bad translations?

13 Example: English  Hebrew I wash the floor אני רוחץ את המכוניתאני שוטפת את הרצפה I wash the car (I wash the floor) (I wash the car) FeminineMasculine Are these bad translations? No. These are actually quite good. No gender information in source. Target must indicate gender.  translator uses world knowledge.

14 Let’s have some fun

15 Language Models as Social Indicators I love her I love him

16 Language Models as Social Indicators I love her I love him אני אוהב אותה אני אוהבת אותו

17 Language Models as Social Indicators I love her I love him I love meat I love vegetables אני אוהב אותה אני אוהבת אותו

18 Language Models as Social Indicators I love her I love him I love meat I love vegetables אני אוהב אותה אני אוהבת אותו אני אוהב בשר אני אוהבת ירקות

19 Language Models as Social Indicators I love her I love him I love meat I love vegetables I love to eat I love to cook אני אוהב אותה אני אוהבת אותו אני אוהב בשר אני אוהבת ירקות

20 Language Models as Social Indicators I love her I love him I love meat I love vegetables I love to eat I love to cook אני אוהב אותה אני אוהבת אותו אני אוהב בשר אני אוהבת ירקות אני אוהב לאכול אני אוהבת לבשל

21 Language Models as Social Indicators I love her I love him I love meat I love vegetables I love to eat I love to cook I love hash I love marijuana אני אוהב אותה אני אוהבת אותו אני אוהב בשר אני אוהבת ירקות אני אוהב לאכול אני אוהבת לבשל

22 Language Models as Social Indicators I love her I love him I love meat I love vegetables I love to eat I love to cook I love hash I love marijuana אני אוהב אותה אני אוהבת אותו אני אוהב בשר אני אוהבת ירקות אני אוהב לאכול אני אוהבת לבשל אני אוהב חשיש אני אוהבת מריחואנה

23 Language Models as Social Indicators I hate him. אני שונאת אותו.

24 Language Models as Social Indicators I hate him. I hate her. אני שונאת אותו.

25 Language Models as Social Indicators I hate him. I hate her. אני שונאת אותו. אני שונאת אותה.

26 Language Models as Social Indicators I hate him. I hate her. I hate him אני שונאת אותו. אני שונאת אותה. אני שונא אותו

27 Language Models as Social Indicators I hate him. I hate her. I hate him אני שונאת אותו. אני שונאת אותה. אני שונא אותו Really? A dot?! Not very stable…

28 Language Models as Social Indicators I hate him. I hate her. I hate him I hate her אני שונאת אותו. אני שונאת אותה. אני שונא אותו אני שונאת אותה Really? A dot?! Not very stable…

29 Language Models as Social Indicators I hate him. I hate her. I hate him I hate her אני שונאת אותו. אני שונאת אותה. אני שונא אותו אני שונאת אותה Really? A dot?! Not very stable… Hmm… is there a message here after all?

30 I love I hate Language Models as Social Indicators

31 I love I hate Language Models as Social Indicators אני אוהב אני שונאת

32 Back to Machine Translation

33 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

34 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

35 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

36 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

37 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

38 One English  Many Hebrew love אוהב אוהבת אוהבים אוהבות Need to acquire more knowledge … use larger parallel corpora … use dictionaries … use FSAs to model inflections Let’s assume this is solved

39 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW??

40 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW??

41 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW??

42 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide.

43 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW??

44 When translating into an MRL: Many possible word forms – Hard to acquire [but assume its solved] Need to choose correct inflection

45 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW??

46 One English  Many Hebrew Hebrew  English: easy אוהבת אני  I love אוהב אני  I love אוהבים אנו  We love אוהבות אנו  We love English  Hebrew: hard I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Don’t worry about it. Just say “love”. The reader will decide. Which form to choose? Translator must decide. HOW?? Just choose one at random? In the worst case we’ll insult someone..

47 Hebrew Verbs agree with Subject on gender and number Hebrew Fact 2

48 I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות Agreement dictates form

49 I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות singular Agreement dictates form

50 I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות singular plural Agreement dictates form

51 I love?  אני אוהב ?  אני אוהבת ?  אני אוהבים ?  אני אוהבות singular plural ✔ ✔ ✘ ✘ Agreement dictates form

52 The girls love?  הבחורות אוהב ?  הבחורות אוהבת ?  הבחורות אוהבים ?  הבחורות אוהבות plural / fem no missing information

53 The girls love?  הבחורות אוהב ?  הבחורות אוהבת ?  הבחורות אוהבים ?  הבחורות אוהבות plural / fem no missing information plural / fem sing / masc plural / masc sing / fem plural / fem

54 The girls love?  הבחורות אוהב ?  הבחורות אוהבת ?  הבחורות אוהבים ?  הבחורות אוהבות plural / fem ✘ no missing information ✘ ✔ ✘ plural / fem sing / masc plural / masc sing / fem plural / fem

55 When translating into an MRL: Many possible word forms – Hard to acquire [but assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word

56 Back to Google Translate The boy washes the car The girl washes the car

57 Back to Google Translate The boy washes the car הנער רוחץ את המכונית The girl washes the car הילדה רוחצת את המכונית

58 Back to Google Translate The boy washes the car הנער רוחץ את המכונית The girl washes the car הילדה רוחצת את המכונית ✔

59 Back to Google Translate The boy washes the car הנער רוחץ את המכונית The girl washes the car הילדה רוחצת את המכונית Good job Franz! ✔

60 Back to Google Translate The boy washes the car הנער רוחץ את המכונית The girl washes the car הילדה רוחצת את המכונית Good job Franz?

61 Back to Google Translate The boy washes the car הנער רוחץ את המכונית The girl washes the car הילדה רוחצת את המכונית Good job Franz? childyoung-man

62 Back to Google Translate The boy with the sunglasses washes the floor הנער עם משקפי השמש שוטפת את הרצפה Good job Franz? The girl with the sunglasses washes the car הבחורה עם משקפי השמש שוטף את המכונית chick

63 Back to Google Translate The boy with the sunglasses washes the floor הנער עם משקפי השמש שוטפת את הרצפה Good job Franz? The girl with the sunglasses washes the car הבחורה עם משקפי השמש שוטף את המכונית

64 Back to Google Translate The boy with the sunglasses washes the floor הנער עם משקפי השמש שוטפת את הרצפה Good job Franz? The girl with the sunglasses washes the car הבחורה עם משקפי השמש שוטף את המכונית ✘ ✘

65 Back to Google Translate The boy with the sunglasses washes the floor הנער עם משקפי השמש שוטפת את הרצפה Good job Franz? The girl with the sunglasses washes the car הבחורה עם משקפי השמש שוטף את המכונית young-man chick ✘ ✘

66 What happened? Long distance agreement Can’t be represented in phrase-table Can’t be represented in n-gram LM  Local “semantic” information from LM/Phrase  Bad translation (ungrammatical)

67 What happened? Long distance agreement Can’t be represented in phrase-table Can’t be represented in n-gram LM  Local “semantic” information from LM/Phrase  Bad translation (ungrammatical) It’s not Franz’s fault, but the system’s

68 When translating into an MRL: Many possible word forms – Hard to acquire [but I assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word and frequently far away from it

69 When translating into an MRL: Many possible word forms – Hard to acquire [but I assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word and frequently far away from it Distance from Verb to Subject in the Hebrew Dependency Treebank (news domain) S-V Dep-LengthCountPercent % % % 44055% 52974% > %

70 When translating into an MRL: Many possible word forms – Hard to acquire [but I assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word and frequently far away from it Distance from Verb to Subject in the Hebrew Dependency Treebank (news domain) S-V Dep-LengthCountPercent % % % 44055% 52974% > % 2 words apart is already though for reliably estimating in an n-gram based system!

71 When translating into an MRL: Many possible word forms – Hard to acquire [but I assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word and frequently far away from it

72 When translating into an MRL: Many possible word forms – Hard to acquire [but I assume its solved] Need to choose correct inflection Inflection is determined based on information which is external to the word and frequently far away from it  Phrase based + N-gram LM can’t do it

73 What if both languages are MRLs? Gender/number marked on both sides No need for word-external information We can translate word  word again MRL  MRL is easy!

74 What if both languages are MRLs? Gender/number marked on both sides No need for word-external information We can translate word  word again MRL  MRL is easy! Wrong!

75 What if both languages are MRLs? Gender/number marked on both sides But: – agreement patterns differ between languages – gender information differ between languages

76 What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement

77 What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement – new shirt חולצה חדשה nueva camisa – new car מכונית חדשה nuevo automovil

78 What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement – new shirt חולצה חדשה nueva camisa – new car מכונית חדשה nuevo automovil

79 – new computer מחשב חדש nueva computadora What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement – new shirt חולצה חדשה nueva camisa – new car מכונית חדשה nuevo automovil

80 – new computer מחשב חדש nueva computadora What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement – new shirt חולצה חדשה nueva camisa – new car מכונית חדשה nuevo automovil חדש nuevo חדשה nueva

81 – new computer מחשב חדש nueva computadora What if both languages are MRLs? Example: – Spanish and Hebrew have adjective-noun agreement – new shirt חולצה חדשה nueva camisa – new car מכונית חדשה nuevo automovil חדש nuevo חדשה nueva - Many-to-many mapping - Correct form still depends on external information - More chances for error - Acquiring all the pairs from parallel corpora is harder

82 Must consider (at least) syntax Phrase-tables and n-grams still can’t do it

83 When Translating into an MRL: MT systems must be aware of gender/number Should have a notion of agreement Use syntax to enforce agreement

84 Source-side Syntax washes floor the The boy with the sunglasses subjobj

85 Source-side Syntax washes floor the The boy with the sunglasses subjobj נער ילד בחור masc/sing

86 Source-side Syntax washes floor the The boy with the sunglasses subjobj נער ילד בחור שוטף שוטפת שוטפים שוטפות masc/sing fem /sing masc/plural fem /plural

87 Source-side Syntax washes floor the The boy with the sunglasses subjobj נער ילד בחור שוטף שוטפת שוטפים שוטפות masc/sing fem /sing masc/plural fem /plural Agree!

88 Source-side Syntax washes floor the The boy with the sunglasses subjobj נער ילד בחור שוטף שוטפת שוטפים שוטפות masc/sing fem /sing masc/plural fem /plural Agree!

89 Source-side Syntax washes floor the The boy with the sunglasses subjobj נער ילד בחור שוטף שוטפת שוטפים שוטפות masc/sing fem /sing masc/plural fem /plural Problems: - How to obtain gender/number information? - How to decode efficiently? - Agreement behavior is not always that simple

90 Target-side Syntax xLNT transducers can model agreement

91 Target-side Syntax NN masc/sing ( ילד )  boy VB masc/sing ( שוטף )  washes VB fem/sing ( שוטפת )  washes NP masc/sing (x0:DT x1:NN masc/sing x2:PP)  x0 x1 x2 VP masc/sing (x0:VB masc/sing x1:NP)  x0 x1 S masc/sing (x0:NP masc/sing x1:VP masc/sing )  x0 x1 xLNT transducers can model agreement

92 Target-side Syntax NN masc/sing ( ילד )  boy VB masc/sing ( שוטף )  washes VB fem/sing ( שוטפת )  washes NP masc/sing (x0:DT x1:NN masc/sing x2:PP)  x0 x1 x2 VP masc/sing (x0:VB masc/sing x1:NP)  x0 x1 S masc/sing (x0:NP masc/sing x1:VP masc/sing )  x0 x1 Gender and number information encoded in the lexical rules xLNT transducers can model agreement

93 Target-side Syntax NN masc/sing ( ילד )  boy VB masc/sing ( שוטף )  washes VB fem/sing ( שוטפת )  washes NP masc/sing (x0:DT x1:NN masc/sing x2:PP)  x0 x1 x2 VP masc/sing (x0:VB masc/sing x1:NP)  x0 x1 S masc/sing (x0:NP masc/sing x1:VP masc/sing )  x0 x1 Gender and number information encoded in the lexical rules Agreement information encoded in the grammar xLNT transducers can model agreement

94 Target-side Syntax NN masc/sing ( ילד )  boy VB masc/sing ( שוטף )  washes VB fem/sing ( שוטפת )  washes NP masc/sing (x0:DT x1:NN masc/sing x2:PP)  x0 x1 x2 VP masc/sing (x0:VB masc/sing x1:NP)  x0 x1 S masc/sing (x0:NP masc/sing x1:VP masc/sing )  x0 x1 Gender and number information encoded in the lexical rules Agreement information encoded in the grammar xLNT transducers can model agreement Problems: - How to obtain gender/number information? - Grammar is going to be huge (can we make it smaller?) - How are we going to obtain the grammar? efficiently encoding morphological processes in a treebank grammar  an open research question

95 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

96 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

97 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

98 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

99 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

100 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better 

101 On The Parsing Side of Things Most work on parsing MRLs: – consider morphology to be a lexicon-level issue Many inflections  high OOV rate – Ignoring morphology at syntax-level – PCFGLA works frustratingly well Recently: – Smarter modeling of morphology at syntax level – Using morphological agreement to improve parsing  Modest benefits to parsing accuracy  PCFGLA still better  PCFGLA is not modeling agreement

102 Rebels without a cause? Syntax-based MT: – Neat! – Only marginally better than phrase-based

103 Rebels without a cause? Syntax-based MT: – Neat! – Only marginally better than phrase-based English grammaticality is relatively easy to capture using local information

104 Rebels without a cause? Syntax-based MT: – Neat! – Only marginally better than phrase-based Syntax-level morphology in parsing: – Neat! – Only marginally better than ignoring it English grammaticality is relatively easy to capture using local information

105 Rebels without a cause? Syntax-based MT: – Neat! – Only marginally better than phrase-based Syntax-level morphology in parsing: – Neat! – Only marginally better than ignoring it English grammaticality is relatively easy to capture using local information I should work harder. Not many agreement mistakes to begin with. Agreement is a generation issue more than a parsing one.

106 Rebels without a cause? Syntax-based MT: – Neat! – Only marginally better than phrase-based Syntax-level morphology in parsing: – Neat! – Only marginally better than ignoring it Both are crucial for translating into MRLs!

107 To Conclude Translating into MRLs brings new challenges Syntax is crucial – If you are not looking into syntax, you should! – If you are looking into syntax – look deeper! Plenty of interesting work to be done

108 To Conclude Translating into MRLs brings new challenges Syntax is crucial – If you are not looking into syntax, you should! – If you are looking into syntax – look deeper! Plenty of interesting work to be done – Finishing up my phd on parsing Looking for a postdoc position for next year


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