運用關係標記與子句邊界偵測作為解決機器翻譯重排序問題關鍵之研究

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研究生：	盧世凱 Shih-Kai Lu
論文名稱：	運用關係標記與子句邊界偵測作為解決機器翻譯重排序問題關鍵之研究 A Study of the Relational Marking and Clause Boundary Detection as the Keys to Solving Reordering Problems in Machine Translation Systems
指導教授：	陳献忠 Shian-Jung Chen
口試委員:	王世平 Shih-Ping Wang 高照明 Zhao-Ming Gao
學位類別：	碩士 Master
系所名稱：	人文社會學院 - 應用外語系 Department of Applied Foreign Languages
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	224
中文關鍵詞：	機器翻譯、字詞結構重排序、關係標誌符號、子句邊界界定、自然語言剖析、表關係的文法功能字、名物化動詞、英文造句法、格位格框語法、語意表達、語意為本的翻譯
外文關鍵詞：	machine language (MT), reordering, relational marker, clause boundary detection (CBD), language parsing, relational function word, meaning-based translation, case frame building, nominalized event, meaning representation, sentence construction principles
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上一筆

翻譯過程中不變的東西才是兩個語言溝通的關鍵。翻譯前後，字詞格式與結構通常不該堅持沿用或延續，應該要保留的是訊息或語意。語意基本上可區分為指稱、界定與關係三種：指稱語意指的是文本所指涉的人事時地物，界定語意指的是文本所指涉的人事時地物的界定範圍，關係語意指的是文本所指涉的人事時地物之間的各種關係。
本研究證明目前機器翻譯研究與開發的主要瓶頸在於字詞結構的重排序，並且找出解決該問題的關鍵。本研究首先使用自己設計的翻譯人工評量方法，根據語意翻譯原則，利用人事時地物指稱必須一直形成一體的概念，分析重排序所造成的問題，並追究造成問題的原因。
本研究將證明字詞結構重排序的在於關係語意，指稱人事時地物之間的關係決定字詞的前後排序。英文表關係的文法功能字、名物化動詞、動詞的字尾、以及事件的格框等乃是表達關係語意的標誌符號。本研究使用擅長利用上下文義的語言剖析器找出文句中的指稱以及指稱間的關係，研究者將進一步示範本文的分析方法以及剖析器的分析結果對譯後修訂非常有幫助。本研究的最終目標在點出解決翻譯字詞結構重排序問題所需的關係標誌符號，可以用翻譯前階段工程的自然語言剖析器分析取得並支援。

Meaning-based human translation insists on the preservation of meaning rather than form and structure. Its advocates believe that meaning or message stays the same before and after translation, and there are essentially three aspects of meaning, referential meaning to stand for entities and events (together as referents), specificational meaning for the specification of referents, and relational meaning for how referents are related or linked. They believe that TL (target language) generation should base reordering on relational meaning, namely on how entities or events are related, which can only get clues from SL (Source Language) analysis, by extracting relations between referents.
This study starts with a manual translation evaluation method that is better equipped to diagnose reordering errors and show those errors are made because pieces of information buried in relational function words, case frame building, nominalized events, verbs ending with –ed and –ing, and sentence construction are ignored or missed. Further investigation would reveal how those relational markers can be found by a language parser that is capable of doing NP finding, PP attachment, Infinitival attachment, scope of coordination resolution, clause boundary detection, and other kinds of relation finding. The study would then end up with some sort of post-editing that makes use of relational marking to perform reordering.

Table of Contents

List of Tables and Figures	vii
Abbreviation	ix
中文摘要	i
ABSTRACT	ii
Chapter One INTRODUCTION	1
1.1 BACKGROUND OF THE STUDY	2
1.2 RULE-BASED APPROACHES OR STATISTICAL APPROACHES	3
1.3 WHY SMT IS GOOD AT WSD	3
1.4 WHY SMT IS NOT GOOD AT GETTING THE WORD ORDER RIGHT	4
1.5 SL RELATIONAL MARKERS AND TL RELATIONAL MARKERS	5
1.6 MEANING-BASED TRANSLATION AND MEANING REPRESENTATION	5
1.7 USE OF LINGUISTIC KNOWLEDGE IN SMT	6
1.8 SIGNIFICANCE OF THE STUDY	6
1.9 PURPOSE OF THE STUDY	6
1.10 RESEARCH QUESTIONS	7
1.11 ORGANIZATION OF THE STUDY	7
Chapter Two LITERATURE REVIEW	9
2.1 MACHINE TRANSLATION	10
2.2 MT HISTORY AND DIFFERENT APPROACHES	10
2.3 RULE-BASED APPROACHES	11
2.4 CORPUS-BASED APPROACHES	13
2.4.1 Statistical MT	14
2.4.2 Phrase-based Translation	14
2.5 MT EVALUATION	14
2.6 AUTOMATIC MT EVALUATION	15
2.6.1 BLEU	15
2.6.2 NIST	15
2.6.3 WER and PER	16
2.7 MT PROBLEMS	16
2.7.1 New Challenges	16
2.7.2 Lexical Ambiguity and Word Sense Disambiguation (WSD)	17
2.7.3 Reordering	17
2.8 MT REORDERING RESEARCH	18
2.9 MEANING-BASED TRANSLATION AND MEANING REPRESENTATION	20
2.10 CONTENT WORDS AND FUNCTION WORDS	21
2.11 SENTENCE CONSTRUCTION PRINCIPLES	21
2.12 CASE GRAMMAR	23
2.13 CLAUSE BOUNDARY DETECTION	23
2.14 LANGUAGE PARSING	24
2.15 CONTEXT GRAMMAR PARSER	24
2.16 SUMMARY	26
Chapter Three RESEARCH METHODOLOGY	28
3.1 A LINGUISTICS APPROACH TO TRANSLATION	28
3.2 THREE ASPECTS OF MEANING	29
3.3 RELATIONAL MEANING AND CONSTITUENT ORDER	30
3.4 CASE FRAME FOR AN EVENT AND THEIR ROLE IN REORDERING	31
3.5 NOMINALIZED EVENTS AND REORDERING	32
3.6 SENTENCE CONSTRUCTION PRINCIPLES AND THE PROBLEMS OF MT REORDERING	32
3.7 DIFFERENT LEVELS OF REORDERING -- WORD SWAPPING,	32
PHRASE REORDERING AND CLAUSE REORDERING	32
3.8 CONSTITUENCY, CLAUSE BOUNDARY DETECTION, AND RELATION FINDING	37
3.9 RELATIONAL MEANING AND LANGUAGE SPECIFIC WORD ORDER	37
3.10 INTERFACE BETWEEN SYNTAX AND SEMANTICS	38
3.11 ROOTS OF MT REORDERING PROBLEMS WHEN ENGLISH IS THE SOURCE LANGUAGE	39
3.12 RELATIONAL MARKING AND REORDERING	40
3.13 BUILDING OF A LANGUAGE PARSER THAT CAN UNEARTH RELATIONAL MARKERS	42
3.14 DIFFERENT KINDS OF AMBIGUITY	45
3.14.1 POS Ambiguity	45
3.14.2 Inseparable POS Tagging is Embedded in the Parser	45
3.14.3 Words that are Categorically more Ambiguous	45
3.14.4 Between Prepositions and Particles	46
3.14.5 Between Prepositions and Subordinate Conjunctions	46
3.14.6 Between Relative Pronouns and Demonstrative Pronouns or other Similar Complementizers	47
3.14.7 Between Nouns and Verbs	47
3.14.8 Between Adjectives or Adverbs and other Parts of Speech	48
3.14.9 Between Function Words and other Parts of Speech	48
3.14.10 Words ending with –ed or –ing	48
3.14.11 Another Reminder of the Significance of POS Problems	49
3.14.12 Form, Distribution, and Function for POS Disambiguation	49
3.15 CONSTITUENCY AND DIFFERENT KINDS OF BOUNDARY DETECTION	50
3.15.1 Where Each Type of Phrase Begins and Ends	51
3.15.2 Where Each Type of Clause Begins and Ends	52
3.15.3 Case Role Assignment and Building of Case Frames for Clause Boundary Detection	52
3.16 CONSTITUENCY AND CONTEXT GRAMMAR PARSER NETWORKS	53
3.17 EVENT-CENTERED PROCESSING, CASE FRAME BUILDING AND RELATION FINDING	67
3.18 ENGLISH SENTENCE CONSTRUCTION PRINCIPLES AS THE LINKS              BETWEEN STRUCTURAL RELATIONS AND CONCEPTUAL RELATIONS	68
3.19 NP FINDING, CLAUSE BOUNDARY DETECTION AND CASE FRAME BUILDING	70
3.20 USING OF RELATIONAL MARKERS TO DO POST-EDITING	75
Chapter Four REORDERING ERRORS IN MT AND THE ORIGIN OF THE PROBLEM	77
4.1 CONTENT WORDS, FUNCTION WORDS AND TRANSLATION EVALUATION	77
4.2 WORD BY WORD EVALUATION:	80
4.3 SUMMARY OF WORD-BASED EVALUATION	136
4.4 CONSTITUENT-BASED EVALUATION	137
4.5 EVENTS, ATTACHMENT, NOMINALIZATION, AND SENTENCE CONSTRUCTION	162
4.6 MT EVALUATION AND POST-EDITING	162
4.7 SUMMARY	164
Chapter Five PARSER OUTPUT AND DISCUSSION	166
5.1 PARSE RESULTS AND DISCUSSION	166
5.2 SUMMARY	198
Chapter Six CONCLUSION	199
6.1 RESEARCH QUESTIONS REVISITED	199
6.2 FINAL REMARK AND FUTURE RESEARCH	200
Reference	202
 
List of Tables and Figures

TABLES:

TABLE 3-1: CONSTRUCTION PRINCIPLE	69
TABLE 3-2: NP FINDING	71
TABLE 3-3: CLAUSE BOUNDARY DETECTION AND CASE FRAME BUILDING	74
TABLE 3-4: CLAUSE BOUNDARY DETECTION AND CASE FRAME BUILDING	75
TABLE 4-1: EVALUATION OF EXAMPLE 1	79
TABLE 4-2: EVALUATION OF EXAMPLE 2	82
TABLE 4-3: EVALUATION OF EXAMPLE 3	84
TABLE 4-4: EVALUATION OF EXAMPLE 4	86
TABLE 4-5: EVALUATION OF EXAMPLE 5	88
TABLE 4-6: EVALUATION OF EXAMPLE 6	91
TABLE 4-7: EVALUATION OF EXAMPLE 7	94
TABLE 4-8: EVALUATION OF EXAMPLE 8	96
TABLE 4-9: EVALUATION OF EXAMPLE 9	99
TABLE 4-10: EVALUATION OF EXAMPLE 10	103
TABLE 4-11: EVALUATION OF EXAMPLE 11	109
TABLE 4-12: EVALUATION OF EXAMPLE 12	111
TABLE 4-13: EVALUATION OF EXAMPLE 13	113
TABLE 4-14: EVALUATION OF EXAMPLE 14	115
TABLE 4-15: EVALUATION OF EXAMPLE 15	117
TABLE 4-16: EVALUATION OF EXAMPLE 16	119
TABLE 4-17: EVALUATION OF EXAMPLE 17	121
TABLE 4-18: EVALUATION OF EXAMPLE 18	122
TABLE 4-19: EVALUATION OF EXAMPLE 19	124
TABLE 4-20: EVALUATION OF EXAMPLE 20	126
TABLE 4-21: EVALUATION OF EXAMPLE 21	128
TABLE 4-22: EVALUATION OF EXAMPLE 22	129
TABLE 4-23: EVALUATION OF EXAMPLE 23	131
TABLE 4-24: EVALUATION OF EXAMPLE 24	133
TABLE 4-25: EVALUATION OF EXAMPLE 25	135
TABLE 4-26: EVALUATION RESULTS	136

FIGURES:

FIGURE 2-1 VAUQUOIS TRIANGLE	12
FIGURE 3-1 SCREEN SHOT	61
FIGURE 5-1 PARSING EXAMPLE 1	167
FIGURE 5-2 PARSING EXAMPLE 2	168
FIGURE 5-3 PARSING EXAMPLE 3	169
FIGURE 5-4 PARSING EXAMPLE 4	170
FIGURE 5-5 PARSING EXAMPLE 5	170
FIGURE 5-6 PARSING EXAMPLE 6	171
FIGURE 5-7 PARSING EXAMPLE 7	173
FIGURE 5-8 PARSING EXAMPLE 8	174
FIGURE 5-9 PARSING EXAMPLE 9	176
FIGURE 5-10 PARSING EXAMPLE 10	177
FIGURE 5-11 PARSING EXAMPLE 11	180
FIGURE 5-12 PARSING EXAMPLE 12	183
FIGURE 5-13 PARSING EXAMPLE 13	184
FIGURE 5-14 PARSING EXAMPLE 14	185
FIGURE 5-15 PARSING EXAMPLE 15	186
FIGURE 5-16 PARSING EXAMPLE 16	188
FIGURE 5-17 PARSING EXAMPLE 17	189
FIGURE 5-18 PARSING EXAMPLE 18	190
FIGURE 5-19 PARSING EXAMPLE 19	191
FIGURE 5-20 PARSING EXAMPLE 20	192
FIGURE 5-21 PARSING EXAMPLE 21	193
FIGURE 5-22 PARSING EXAMPLE 22	194
FIGURE 5-23 PARSING EXAMPLE 23	195
FIGURE 5-24 PARSING EXAMPLE 24	196
FIGURE 5-25 PARSING EXAMPLE 25	197

                                

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