generation in machine translation from deep syntactic
play

Generation in Machine Translation from Deep Syntactic Trees Keith - PowerPoint PPT Presentation

May 22, 2023 •129 likes •544 views

Generation in Machine Translation from Deep Syntactic Trees Keith Hall Petr N mec Johns Hopkins University Charles University in Prague Outline Transfer-based MT Tectogrammatical Representation (TR) (deep syntax) Generation from

  1. Generation in Machine Translation from Deep Syntactic Trees Keith Hall Petr N ě mec Johns Hopkins University Charles University in Prague

  2. Outline ● Transfer-based MT ● Tectogrammatical Representation (TR) (deep syntax) ● Generation from English TR trees ● process ● models ● Empirical results SSST ‘07 - Hall & N ě mec

  3. Transfer-based MT Source Target (Czech) (English) SSST ‘07 - Hall & N ě mec

  4. Transfer-based MT Source Target (Czech) (English) SSST ‘07 - Hall & N ě mec

  5. Transfer-based MT Interlingua Source Target (Czech) (English) SSST ‘07 - Hall & N ě mec

  6. Transfer-based MT Interlingua Source Target (Czech) (English) SSST ‘07 - Hall & N ě mec

  7. Transfer-based MT Tectogrammar Source Target (Czech) (English) SSST ‘07 - Hall & N ě mec

  8. Tecto Transfer-based MT Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  9. Tecto Transfer-based MT deep deep syntax syntax (Czech Tecto) (English Tecto) surface surface syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  10. Tecto Transfer-based MT deep deep syntax syntax (Czech Tecto) (English Tecto) g n i s r a surface surface p syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  11. Tecto Transfer-based MT tree transduction deep deep syntax syntax (Czech Tecto) (English Tecto) g n i s r a surface surface p syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  12. Tecto Transfer-based MT tree transduction deep deep syntax syntax (Czech Tecto) (English Tecto) generation g n i s r a surface surface p syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  13. Tecto Transfer-based MT tree transduction deep deep syntax syntax (Czech Tecto) (English Tecto) generation g n i s r a surface surface ` p syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  14. Tecto Transfer-based MT tree transduction deep deep syntax syntax (Czech Tecto) (English Tecto) generation g n i s ? r a surface surface ` p syntax syntax Czech English sentence sentence SSST ‘07 - Hall & N ě mec

  15. Transfer-based MT ● Allows us to explore deep syntactic representations ● Factored models are clear ● Need not be greedy one-best process ● although we present one-best generation/results SSST ‘07 - Hall & N ě mec

  16. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” FORM: #2 LEMM: # FUNC: SENT FORM: opened LEMM: open FUNC: PRED POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  17. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” FORM: #2 LEMM: # FUNC: SENT FORM: opened LEMM: open FUNC: PRED POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  18. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” lemma FORM: #2 LEMM: # FUNC: SENT FORM: opened LEMM: open FUNC: PRED POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  19. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” FORM: #2 LEMM: # FUNC: SENT functor FORM: opened LEMM: open FUNC: PRED POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  20. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” FORM: #2 LEMM: # FUNC: SENT part-of-speech FORM: opened LEMM: open FUNC: PRED POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  21. Tectogrammatical Representation “Now the network has opened a news bureau in the Hungarian capital” FORM: #2 LEMM: # FUNC: SENT FORM: opened LEMM: open FUNC: PRED tense & mood POS: 'VBN' T_M: 'SIM'_'IND' FORM: network FORM: Now FORM: bureau FORM: capital LEMM: network LEMM: now LEMM: bureau LEMM: capital FUNC: ACT FUNC: TWHEN FUNC: PAT FUNC: LOC POS: 'NN' POS: 'RB' POS: 'NN' POS: 'NN' FORM: news FORM: Hungarian LEMM: news LEMM: hungarian FUNC: RSTR FUNC: RSTR POS: 'NN' POS: 'JJ' SSST ‘07 - Hall & N ě mec

  22. Generation Process deep 1. Insert syn-semantic (function) words syntax (English Tecto) 2. Subtree reordering ● Intermediary surface syntax ? surface ● Reordering constraints? syntax ● maximum subtree size ● coordination English sentence SSST ‘07 - Hall & N ě mec

  23. Generation Model arg max A,f P ( A, f | T ) = arg max A,f P ( f | A, T ) P ( A | T ) ≈ arg max P ( f | T, arg max A P ( A | T )) f ● tecto nodes: T = { t 1 , . . . , t i , . . . , t n } ● insertion string: A = { a 1 , . . . , a i , . . . , a k } n ≤ k ≤ 2 n ● order mapping: f : { A ∪ T } → { 1 , . . . , 2 n } SSST ‘07 - Hall & N ě mec

  24. Generation Model Insertion arg max A,f P ( A, f | T ) = arg max A,f P ( f | A, T ) P ( A | T ) ≈ arg max P ( f | T, arg max A P ( A | T )) f ● tecto nodes: T = { t 1 , . . . , t i , . . . , t n } ● insertion string: A = { a 1 , . . . , a i , . . . , a k } n ≤ k ≤ 2 n ● order mapping: f : { A ∪ T } → { 1 , . . . , 2 n } SSST ‘07 - Hall & N ě mec

  25. Generation Model Reordering arg max A,f P ( A, f | T ) = arg max A,f P ( f | A, T ) P ( A | T ) ≈ arg max P ( f | T, arg max A P ( A | T )) f ● tecto nodes: T = { t 1 , . . . , t i , . . . , t n } ● insertion string: A = { a 1 , . . . , a i , . . . , a k } n ≤ k ≤ 2 n ● order mapping: f : { A ∪ T } → { 1 , . . . , 2 n } SSST ‘07 - Hall & N ě mec

  26. Insertion Process “Now the network has opened a news bureau in the Hungarian capital” open PRED VBN SIM_IND network now bureau capital ACT TWHEN PAT LOC NN RB NN NN news hungarian RSTR RSTR NN JJ SSST ‘07 - Hall & N ě mec

  27. Insertion Process “Now the network has opened a news bureau in the Hungarian capital” open PRED VBN SIM_IND network now bureau capital has ACT TWHEN PAT LOC AUX NN RB NN NN the DT news hungarian in RSTR PP RSTR NN JJ the a DT DT SSST ‘07 - Hall & N ě mec

  28. Insertion Model P ( A | T ) � = P ( a i | a 1 , . . . , a i − 1 , T ) i � ≈ P ( a i | t i , t g ( i ) ) i ● Insertion is dependent on local context: ● tecto node (includes: lemma, functor, POS) ● parent node ● Three independent models: ● articles ● prepositions and subordinating conjunctions ● modals (deterministic, given functor) SSST ‘07 - Hall & N ě mec

  29. Reordering Process “Now the network has opened a news bureau in the Hungarian capital” open PRED VBN SIM_IND network bureau capital now has ACT PAT LOC TWHEN AUX NN NN NN RB SSST ‘07 - Hall & N ě mec

  30. Reordering Process “Now the network has opened a news bureau in the Hungarian capital” open PRED VBN SIM_IND network has bureau capital now ACT AUX PAT LOC TWHEN NN NN NN RB SSST ‘07 - Hall & N ě mec

  31. Reordering Process “Now the network has opened a news bureau in the Hungarian capital” network open bureau capital has now ACT PRED PAT LOC AUX TWHEN NN VBN NN NN RB SIM_IND SSST ‘07 - Hall & N ě mec

Recommend

Machine Translation Steps: Analysis, Transfer, Generation Classical and Statistical

Machine Translation Steps: Analysis, Transfer, Generation Classical and Statistical

Session 2: Syntactic Transfer Syntactic Transfer Machine Translation Steps: Analysis, Transfer, Generation Classical and Statistical Approaches How are the various types of divergence dealt with? Session 2: Syntactic Transfer

430 views • 9 slides
Natural Language Processing Syntactic Models Machine Translation III Dan Klein UC Berkeley 1

Natural Language Processing Syntactic Models Machine Translation III Dan Klein UC Berkeley 1

Natural Language Processing Syntactic Models Machine Translation III Dan Klein UC Berkeley 1 2 3 4 Syntactic Decoding 5 6 7 8 Soft Syntactic MT: From Chiang 2010 Flexible Syntax Hiero Rules From [Chiang et al, 2005] 9 10 Lots to

380 views • 18 slides
Decoding and Inference with Syntactic Translation Models Machine Translation Lecture 15

Decoding and Inference with Syntactic Translation Models Machine Translation Lecture 15

Decoding and Inference with Syntactic Translation Models Machine Translation Lecture 15 Instructor: Chris Callison-Burch TAs: Mitchell Stern, Justin Chiu Website: mt-class.org/penn jon-ga ringo-o tabeta jon-ga ringo-o tabeta ringo-o tabeta

1.16k views • 83 slides
Machine Translation: Going Deep Philipp Koehn 4 June 2015 Philipp Koehn Machine Translation:

Machine Translation: Going Deep Philipp Koehn 4 June 2015 Philipp Koehn Machine Translation:

Machine Translation: Going Deep Philipp Koehn 4 June 2015 Philipp Koehn Machine Translation: Going Deep 4 June 2015 How do we Improve Machine Translation? 1 More data Better linguistically motivated models Better machine learning

1.21k views • 67 slides
Syntactic Translation Lattices Felix Stahlberg, Adria de Gispert, Eva Hasler, and Bill Byrne

Syntactic Translation Lattices Felix Stahlberg, Adria de Gispert, Eva Hasler, and Bill Byrne

Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices Felix Stahlberg, Adria de Gispert, Eva Hasler, and Bill Byrne Department of Engineering Neural Machine Translation by Minimising the

1.97k views • 12 slides
Introductory Notes on Machine Translation and Deep Learning February 20, 2017 Jindich

Introductory Notes on Machine Translation and Deep Learning February 20, 2017 Jindich

NPFL116 Compendium of Neural Machine Translation Introductory Notes on Machine Translation and Deep Learning February 20, 2017 Jindich Libovick, Jindich Helcl Charles Univeristy in Prague Faculty of Mathematics and Physics Institute of

532 views • 18 slides
Natural Language Processing Machine Translation III Dan Klein UC Berkeley 1 Syntactic Models

Natural Language Processing Machine Translation III Dan Klein UC Berkeley 1 Syntactic Models

Natural Language Processing Machine Translation III Dan Klein UC Berkeley 1 Syntactic Models 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Syntactic Decoding 29 30 31 32 33 34 35 36

1.24k views • 106 slides
Machine Translation Classification of divergences Classical and Statistical Approaches

Machine Translation Classification of divergences Classical and Statistical Approaches

Session 4: Interlingua-based MT Dorr (1992, 1994): UNITRAN system Machine Translation Classification of divergences Classical and Statistical Approaches Lexical Conceptual Structure Translation mappings between syntactic

130 views • 12 slides
Unsupervised Machine Translation Sachin Kumar Conditional Text Generation Generate text

Unsupervised Machine Translation Sachin Kumar Conditional Text Generation Generate text

CMU CS11-737: Multilingual NLP (Fall 2020) Unsupervised Machine Translation Sachin Kumar Conditional Text Generation Generate text according to a specification: P(Y|X) Input X Output Y (Text) Task English Hindi Machine Translation

743 views • 35 slides
What can Statistical Machine Translation teach Neural Machine Translation about Structured

What can Statistical Machine Translation teach Neural Machine Translation about Structured

What can Statistical Machine Translation teach Neural Machine Translation about Structured Prediction? Graham Neubig @ ICLR Workshop on Deep Reinforcement Learning Meets Structured Prediction 5/6/2019 Types of Prediction Types of

1.73k views • 127 slides
Deep Linguistic Information in Hybrid Machine Translation Charles

Deep Linguistic Information in Hybrid Machine Translation Charles

Deep Linguistic Information in Hybrid Machine Translation Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic Outline: From Data To an MT

251 views • 24 slides
What can Statistical Machine Translation teach Neural Text Generation about Optimization? Graham

What can Statistical Machine Translation teach Neural Text Generation about Optimization? Graham

What can Statistical Machine Translation teach Neural Text Generation about Optimization? Graham Neubig @ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation 6/6/2019 or How to Optimize your Neural Generation

638 views • 34 slides
Machine Translation using Deep Learning Methods Max Fomin Michael Zolotov Sequence to Sequence

Machine Translation using Deep Learning Methods Max Fomin Michael Zolotov Sequence to Sequence

Machine Translation using Deep Learning Methods Max Fomin Michael Zolotov Sequence to Sequence Learning with Neural Networks Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation Topics

909 views • 41 slides
QTL EAP : A PROJECT ON MACHINE TRANSLATION BY DEEP LANGUAGE ENGINEERING APPROACHES A NTNIO B

QTL EAP : A PROJECT ON MACHINE TRANSLATION BY DEEP LANGUAGE ENGINEERING APPROACHES A NTNIO B

QTL EAP : A PROJECT ON MACHINE TRANSLATION BY DEEP LANGUAGE ENGINEERING APPROACHES A NTNIO B RANCO , H ANS U SZKOREIT , A LJOSCHA B URCHARDT , J AN H AJI , M ARTIN P OPEL , K IRIL S IMOV , P ETYA O SENOVA , M ARKUS E GG , E NEKO A GIRRE , G

481 views • 17 slides
Statistical Machine Translation Nadir Durrani 21-November-2014 Machine Translation

Statistical Machine Translation Nadir Durrani 21-November-2014 Machine Translation

Statistical Machine Translation Nadir Durrani 21-November-2014 Machine Translation www.uni-stuttart.de Problem: Automatic translation the foreign text: 2 Open Problems in Machine Translation www.uni-stuttart.de Ambiguity in translation

939 views • 44 slides
Machine Translation 2 Wikipedia Machine translation, often referred to by the acronym MT, is a

Machine Translation 2 Wikipedia Machine translation, often referred to by the acronym MT, is a

Machine Translation 2 Wikipedia Machine translation, often referred to by the acronym MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to

782 views • 24 slides
Machine Translation (M2M) Machine Translation (M2M) SNMP MIB to CIM MOF SNMP MIB to CIM MOF

Machine Translation (M2M) Machine Translation (M2M) SNMP MIB to CIM MOF SNMP MIB to CIM MOF

Machine Translation (M2M) Machine Translation (M2M) SNMP MIB to CIM MOF SNMP MIB to CIM MOF WIMS Working Group CIM Project 19 February 2007 PWG Maui Ira McDonald (High North) M2M Background Background M2M DMTF/PWG

437 views • 7 slides
Natural Language Processing Machine Translation Dan Klein UC Berkeley 1 Machine Translation 2

Natural Language Processing Machine Translation Dan Klein UC Berkeley 1 Machine Translation 2

Natural Language Processing Machine Translation Dan Klein UC Berkeley 1 Machine Translation 2 Machine Translation: Examples 3 Levels of Transfer 4 Word Level MT: Examples la politique de la haine . (Foreign Original) politics

675 views • 43 slides
Inferring Translation Candidates for Multilingual Dictionary Generation with Multi-Way Neural

Inferring Translation Candidates for Multilingual Dictionary Generation with Multi-Way Neural

Inferring Translation Candidates for Multilingual Dictionary Generation with Multi-Way Neural Machine Translation Mihael Arcan, Daniel Torregrosa*, Sina Ahmadi* and John P. McCrae This publication has emanated from research supported in part

591 views • 42 slides
Introd u ction to machine translation MAC H IN E TR AN SL ATION IN P YTH ON Th u shan

Introd u ction to machine translation MAC H IN E TR AN SL ATION IN P YTH ON Th u shan

Introd u ction to machine translation MAC H IN E TR AN SL ATION IN P YTH ON Th u shan Ganegedara Data Scientist and A u thor Machine translation MACHINE TRANSLATION IN PYTHON Machine translation MACHINE TRANSLATION IN PYTHON Co u rse o u

839 views • 38 slides
Natural Language Processing Machine Translation Machine Translation Dan Klein UC Berkeley

Natural Language Processing Machine Translation Machine Translation Dan Klein UC Berkeley

Natural Language Processing Machine Translation Machine Translation Dan Klein UC Berkeley Machine Translation: Examples Levels of Transfer Word Level MT: Examples Phrasal MT: Examples la politique de la haine . (Foreign Original)

223 views • 8 slides
Use of the Machine Translation Module within Dj Vu X2 Quick Guidance Introduction Machine

Use of the Machine Translation Module within Dj Vu X2 Quick Guidance Introduction Machine

Use of the Machine Translation Module within Dj Vu X2 Quick Guidance Introduction Machine Translation has now become incontrovertible in the translation industry. We at Atril put efforts in providing best help to the translation actors. In

393 views • 6 slides
Improving Trees and Alignments for Syntax- Based Machine Translation Kevin Knight

Improving Trees and Alignments for Syntax- Based Machine Translation Kevin Knight

Improving Trees and Alignments for Syntax- Based Machine Translation Kevin Knight USC/Information Sciences Institute joint work with Steven DeNeefe, Daniel Marcu, Wei Wang, and Jonathan May SRI, July 12, 2007 Syntactic Approaches to MT

1.15k views • 64 slides
Code Shape More on Three-address Code Generation cs5363 1 Machine Code Translation A

Code Shape More on Three-address Code Generation cs5363 1 Machine Code Translation A

Code Shape More on Three-address Code Generation cs5363 1 Machine Code Translation A single language construct can have many implementations many-to-many mappings from high-level source language to low-level target machine language

560 views • 16 slides

More recommend