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Semantic Operations for Transfer-Based Machine Translation

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Semantic Operations for Transfer-Based Machine Translation ©Copyright 2018 Michael Wayne Goodman Semantic Operations for Transfer-based Machine Translation Michael Wayne Goodman A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2018 Reading Committee: Emily M. Bender, Chair Francis Bond, Chair Fei Xia Program Authorized to Offer Degree: Linguistics University of Washington Abstract Semantic Operations for Transfer-based Machine Translation Michael Wayne Goodman Co-Chairs of the Supervisory Committee: Chair Emily M. Bender Department of Linguistics Chair Francis Bond Division of Linguistics and Multilingual Studies, Nanyang Technological University This dissertation describes a new approach to the automatic extraction of semantic map- pings (transfer rules) for rule-based machine translation. This approach continues previous work in combining HPSG rule-based grammars, whose precise bidirectional implementation facilitates deep semantic analysis of sentences and the enumeration of grammatical realiza- tions of semantic representations, and data-driven techniques of machine translation, whose automatic extraction of knowledge and statistical inference allow models to be quickly built from bitexts and to rank extracted patterns by their frequency. I define two new methods for bilingually aligning semantic fragments (or semantic subgraphs) and a heuristic strategy for aligning nodes between source and target subgraphs, which together allow me to design transfer systems that meet, and at times exceed, the translation coverage and quality of the prior state of the art with a significantly reduced dependence on idiosyncratic language-pair definitions (i.e., improved language independence). These improvements are made possible by a number of semantic operations, either designed or implemented by me and defined within this dissertation, that fully model the semantic representations and allow for inspec- tion and transformation as graph operations. I apply my methods to the task of translating Japanese sentences into English—a typologically distant language pair. TABLE OF CONTENTS Page List of Figures ....................................... v List of Tables ........................................ viii Glossary ........................................... xi Notational Conventions .................................. xviii Chapter 1: Introduction ................................ 1 1.1 Translating Human Language .......................... 3 1.2 Machine Translation using Semantic Representations ............. 6 1.3 Main Contributions ................................ 10 1.4 Document Outline ................................ 12 Chapter 2: DELPH-IN Semantics ........................... 15 2.1 Philosophical Basics ............................... 17 2.2 Structural Semantic Preliminaries and Terminology .............. 19 2.3 Underspecified Representation .......................... 22 2.4 Visual Presentation of DELPH-IN Semantics .................. 24 2.5 SEM-I: The Semantic Interface ......................... 25 2.6 MRS: Minimal Recursion Semantics ....................... 28 2.7 RMRS: Robust Minimal Recursion Semantics ................. 31 2.8 EDS: Elementary Dependency Structures .................... 31 2.9 DM: Bilexical Dependencies ........................... 33 2.10 DMRS: Dependency Minimal Recursion Semantics .............. 34 2.11 Summary of DELPH-IN Representations .................... 36 2.12 Comparison to Other Frameworks ........................ 36 i 2.13 Chapter Summary ................................ 39 Chapter 3: Machine Translation ............................ 41 3.1 Rule-based Machine Translation ......................... 41 3.2 Statistical and Neural Machine Translation ................... 42 3.3 Chapter Summary ................................ 45 Chapter 4: System Overview .............................. 46 4.1 Translation Pipeline ............................... 46 4.2 Pipeline Information Management ........................ 49 4.3 Parsing ...................................... 50 4.4 Transfer ...................................... 51 4.5 Generation .................................... 56 4.6 Translation Selection ............................... 56 4.7 Chapter Summary ................................ 58 Chapter 5: Semantic Operations ............................ 59 5.1 MRS to DMRS Conversion ............................ 59 5.2 DMRS to MRS Conversion ............................ 64 5.3 Semantic Tests .................................. 68 5.4 Semantic Graph Traversal ............................ 76 5.5 PENMAN Serialization .............................. 82 5.6 Subgraph Extraction ............................... 86 5.7 DMRS Simplification ............................... 90 5.8 Chapter Summary ................................ 95 Chapter 6: Bilingual Semantic Subgraph Alignment ................. 96 6.1 Bilingually Aligned Predicate Phrases ...................... 97 6.2 Top-Down Subgraph Enumeration ........................ 102 6.3 Filtering Subgraphs and Subgraph Pairs .................... 111 6.4 Related Work ................................... 112 6.5 Chapter Summary ................................ 115 ii Chapter 7: Transfer Grammar Augmentation ..................... 116 7.1 The LOGON Transfer Machinery ........................ 117 7.2 Challenges and Strategies in Building Transfer Grammars .......... 121 7.3 Converting Subgraphs to Transfer Rules .................... 124 7.4 Transfer Grammar Maintenance ......................... 131 7.5 Chapter Summary ................................ 132 Chapter 8: Data Exploration .............................. 133 8.1 Data Sources and Divisions ........................... 133 8.2 Basic Analysis ................................... 138 8.3 Analysis of Parsing Performance ......................... 145 8.4 Analysis of Generation Performance ....................... 151 8.5 The Bisem Corpus ................................ 152 8.6 Chapter Summary ................................ 154 Chapter 9: Experimental Design ............................ 155 9.1 Evaluation Metrics ................................ 155 9.2 Pipeline Parameters for Transfer-based Systems ................ 158 9.3 Data Preparation ................................. 159 9.4 Moses Baseline .................................. 160 9.5 Haugereid and Bond Baseline .......................... 162 9.6 LPA: Bilingually-aligned Predicate Phrases ................... 164 9.7 SGA: High-frequency Coincident Subgraphs .................. 170 9.8 Chapter Summary ................................ 179 Chapter 10: Results ................................... 180 10.1 Baseline Development Results .......................... 181 10.2 LPA Development Results ............................ 183 10.3 SGA Development Results ............................ 185 10.4 Combined Test Results .............................. 185 10.5 Translation Examples .............................. 189 10.6 Chapter Summary ................................ 191 iii Chapter 11: Analysis ................................... 192 11.1 Translation Analysis ............................... 192 11.2 Comparing Systems by Automatic Quality Estimations ............ 195 11.3 SGA Subgraph Topologies ............................ 201 11.4 Semantic Analysis ................................ 204 11.5 Performance Comparison ............................. 206 11.6 Translation Examples .............................. 211 11.7 Conclusion ..................................... 215 Chapter 12: Conclusion ................................. 218 12.1 Methodological and Artifactual Contributions ................. 219 12.2 Next Steps and Future Research ......................... 222 12.3 Closing Words ................................... 228 Bibliography ........................................ 229 Appendix A: Setting Up the Translation Environment ................. 245 A.1 Moses ....................................... 245 A.2 ERG ........................................ 247 Appendix B: Database Schema .............................. 249 iv LIST OF FIGURES Figure Number Page 1.1 Vauquois triangle ................................. 7 1.2 Vauquois inverted funnel with a very long spout ................ 8 2.1 MRS graph view for The dog sleeps. ....................... 16 2.2 MRS graph view for Every dog chased some cat. ................ 25 2.3 Variable hierarchy ................................ 26 2.4 MRS fragments of the large and gentle dog sleeps ............... 32 2.5 DMRS fragments of the large and gentle dog sleeps .............. 35 2.6 Ordering of information density among MRS, DMRS, EDS, and DM .... 37 4.1 Simplified translation pipeline .......................... 46 4.2 Pipeline fan-out .................................. 48 4.3 Expanded view of the parsing component .................... 50 4.4 Monolingual MTR in JaEn for normalizing alternate orthographies ..... 53 4.5 Hand-written MTR for the idiomatic 嘘をつく uso-wo tsuku “tell a lie [lit: breathe a lie]” ................................... 53 4.6 Building the transfer pair store ......................... 54 4.7 Expanded view of the transfer component ................... 55 4.8 Expanded view of the generation component .................. 56 4.9 Expanded view of the selection component: First method ........... 57 4.10 Expanded view of the selection component: Oracle method .......... 57 4.11 First-selection of translation hypotheses .................... 57 5.1 MRS for The dog whose tail is long barked. ................... 62 5.2 DMRS for The dog whose
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