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Approaches to Machine Translation

Approaches to Machine Translation. CSC 5930 Machine Translation Fall 2012 Dr. Tom Way. How humans do translation?. Learn a foreign language: Memorize word translations Learn some patterns: Exercise: Passive activity: read, listen Active activity: write, speak Translation:

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Approaches to Machine Translation

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  1. Approaches to Machine Translation CSC 5930 Machine Translation Fall 2012 Dr. Tom Way

  2. How humans do translation? • Learn a foreign language: • Memorize word translations • Learn some patterns: • Exercise: • Passive activity: read, listen • Active activity: write, speak • Translation: • Understand the sentence • Clarify or ask for help (optional) • Translate the sentence Training stage Translation lexicon Templates, transfer rules Reinforced learning? Reranking? Decoding stage Parsing, semantics analysis? Interactive MT? Word-level? Phrase-level? Generate from meaning?

  3. What kinds of resources are available to MT? • Translation lexicon: • Bilingual dictionary • Templates, transfer rules: • Grammar books • Parallel data, comparable data • Thesaurus, WordNet, FrameNet, … • NLP tools: tokenizer, morph analyzer, parser, …  More resources for major languages, less for “minor” languages.

  4. Major approaches • Transfer-based • Interlingua • Example-based (EBMT) • Statistical MT (SMT) • Hybrid approach

  5. The MT triangle Meaning (interlingua) Synthesis Analysis Transfer-based Phrase-based SMT, EBMT Word-based SMT, EBMT word Word

  6. Transfer-based MT • Analysis, transfer, generation: • Parse the source sentence • Transform the parse tree with transfer rules • Translate source words • Get the target sentence from the tree • Resources required: • Source parser • A translation lexicon • A set of transfer rules • An example: Mary bought a book yesterday.

  7. Transfer-based MT (cont) • Parsing: linguistically motivated grammar or formal grammar? • Transfer: • context-free rules? A path on a dependency tree? • Apply at most one rule at each level? • How are rules created? • Translating words: word-to-word translation? • Generation: using LM or other additional knowledge? • How to create the needed resources automatically?

  8. Interlingua • For n languages, we need n(n-1) MT systems. • Interlingua uses a language-independent representation. • Conceptually, Interlingua is elegant: we only need n analyzers, and n generators. • Resource needed: • A language-independent representation • Sophisticated analyzers • Sophisticated generators

  9. Interlingua (cont) • Questions: • Does language-independent meaning representation really exist? If so, what does it look like? • It requires deep analysis: how to get such an analyzer: e.g., semantic analysis • It requires non-trivial generation: How is that done? • It forces disambiguation at various levels: lexical, syntactic, semantic, discourse levels. • It cannot take advantage of similarities between a particular language pair.

  10. Example-based MT • Basic idea: translate a sentence by using the closest match in parallel data. • First proposed by Nagao (1981). • Ex: • Training data: • w1 w2 w3 w4  w1’ w2’ w3’ w4’ • w5 w6 w7  w5’ w6’ w7’ • w8 w9  w8’ w9’ • Test sent: • w1 w2 w6 w7 w9  w1’ w2’ w6’ w7’ w9’

  11. EMBT (cont) • Types of EBMT: • Lexical (shallow) • Morphological / POS analysis • Parse-tree based (deep) • Types of data required by EBMT systems: • Parallel text • Bilingual dictionary • Thesaurus for computing semantic similarity • Syntactic parser, dependency parser, etc.

  12. EBMT (cont) • Word alignment: using dictionary and heuristics  exact match • Generalization: • Clusters: dates, numbers, colors, shapes, etc. • Clusters can be built by hand or learned automatically. • Ex: • Exact match: 12 players met in Paris last Tuesday  12 Spieler trafen sich letzen Dienstag in Paris • Templates: $num players met in $city $time $num Spieler trafen sich $time in $city

  13. Statistical MT • Basic idea: learn all the parameters from parallel data. • Major types: • Word-based • Phrase-based • Strengths: • Easy to build, and it requires no human knowledge • Good performance when a large amount of training data is available. • Weaknesses: • How to express linguistic generalization?

  14. Comparison of resource requirement

  15. Hybrid MT • Basic idea: combine strengths of different approaches: • Syntax-based: generalization at syntactic level • Interlingua: conceptually elegant • EBMT: memorizing translation of n-grams; generalization at various level. • SMT: fully automatic; using LM; optimizing some objective functions. • Types of hybrid HT: • Borrowing concepts/methods: • SMT from EBMT: phrase-based SMT; Alignment templates • EBMT from SMT: automatically learned translation lexicon • Transfer-based from SMT: automatically learned translation lexicon, transfer rules; using LM • … • Using two MTs in a pipeline: • Using transfer-based MT as a preprocessor of SMT • Using multiple MTs in parallel, then adding a re-ranker.

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