Machine Translator

What is Machine Translator?

Automatic translation from one language to another

Koehn: „Translating between languages is a task for which even humans require special training.“

Why Even Humans Require Special Training

وندشی شورای امنیت، مردم ایران رو به قبله نمی « لولو» با نشان دادن

راهابچهكهموجوداتیمثلامنیتشورایاگركهاستگفته*:نیوزویكترجمه.كشندنمیدرازجهانمسلمانانقبلهسویبهایرانمردمشود،ظاهرترسانندمی

همراناكیترسچیزامنیتشورایاگركهگفت*:پائیسالاسپانیایینشریهترجمه.دخوابننمیسعودیعربستانسویبهایرانمردمهمبازدهد،نشانایرانیانبه

مركزسویبهایرانیانكشیدندرازكهگفت*:اومانیتهفرانسوینشریهترجمهایند،بترسنایافسانهموجوداتازآنهاكهدارداینبهبستگیمسلماناناعتقادات

.استایرانیداستانیك

Other Similar Concept

• computer-aided translation

• machine-aided human translation (MAHT)

History

As early as the 17th century by philosophers René Descartes and Gottfried Wilhelm Leibniz

MT in Computer

Applications

Dissemination

Publication inother languages

Communication

Emails, chats

Assimilation

Understand the Content

Challenges

Input

Typology

Lexical

Other

Input: Ambiguity

I saw a man with telescope

Input: Complexity

General relativity includes a dynamical spacetime so it isdifficult to see how to identify the conserved energy andmomentum Noether's theorem allows these quantities to bedetermined from a Lagrangian with translation invariance butgeneral covariance makes translation invariance intosomething of a gauge symmetry

Input: Wrong Sentence

I try for getting best grades but I did not can achive it

Typology: Morphology

Typology: Syntax

order of verbs (V), subjects (S) and objects (O)

Typology: Argument structure and linking

Typology: Pronouns omission

Lexical: Ambiguity

Lexical: Grammer

Lexical: Lexical gap

Lexical: Idiom

Other Challenges

Other Challenges

Human Translation Process

• Decoding the meaning of the source text

• Re-encoding this meaning in the target language.

Simplest Machine Translator

Apple سیب

MT

Human Translation

With Machine Aid

Machine Translation

With Human Aid

Fully Automated Translation

Rule Based MT

Direct MT Transfer MT Interlingua

Knowledge Based MT

Principle Based MT

Empirical Based MT

Statistical MT

Word Based Translation

Phrase Based Translation

Hierarchical Phrase Based

Translation

Example Based MT

Online Interactive MT

Hybrid MT Neural MT

Rule-Base MT

Direct Translation

• dictionary has to cover all cross-lingual phenomena

• need to include contextual information in dictionary (long phrases)

• inflectional agreement, shifts in word order & structure

+ direct translation systems include simplistic rules

Direct Translation Approach

• simplistic: only low-level pre/post-processing (tokenization, etc)

• advanced: handle some specific phenomena

identification & handling of syntactic ambiguity

morphological processing/synthesis

word re-ordering rules

rules for prepositions

handling of compounds and idioms, ...

Is Direct Translation Feasible?

Transfer Based Translation

Motivation:

• complete analysis of source language sentences

• handle lexical & structural ambiguity in one formalism

Transfer Based Needed Information/Tools

• source language parser (morpho-syntactic analysis)

• transfer engine (e.g. unification based grammar)

• target language generator

• Morphological analysis. Surface forms of the input text are classified as to part-of-speech (e.g. noun, verb, etc.) and sub-category (number, gender, tense, etc.). All of the possible "analyses" for each surface form are typically made output at this stage, along with the lemma of the word.

• Lexical categorisation. In any given text some of the words may have more than one meaning, causing ambiguity in analysis. Lexical categorisation looks at the context of a word to try to determine the correct meaning in the context of the input. This can involve part-of-speech tagging and word sense disambiguation.

• Lexical transfer. This is basically dictionary translation; the source language lemma (perhaps with sense information) is looked up in a bilingual dictionary and the translation is chosen.

• Structural transfer. While the previous stages deal with words, this stage deals with larger constituents, for example phrases and chunks. Typical features of this stage include concordance of gender and number, and re-ordering of words or phrases.

• Morphological generation. From the output of the structural transfer stage, the target language surface forms are generated.

Transfer Based: Syntactic Transfer

What are the problems?

• lots of grammar engineering (writing rules ...)

• language-pair specific rules

• exponential ambiguity

• variation & preference

Interlingua-based Translation

Persian

English

Persian

SpanishEnglish

Persian

Spanish

Japanese

English

interlingua

Persian

Spanish

New

English

Advantages & Disadvantages

• no language-pair specific transfer

• simple to add new languages (add new analysis/generation component)

• need to design interlingua that covers all language phenomena

• need semantic representation (and that’s hard!)

Statistical MT

Statistical MT

Statistical MT

Statistical MT

(1) build a language model which allows us to estimate P(e)

(2) build a translation model which allows us to estimate P(f|e)

(3) search for e maximizing the product P(f|e).P(e)

Language Modeling

Which N-Gram?

• 1-Gram is not very realistic

• More realistic still is the trigram model

Problem

50,000 English word

2.5 billion possible bigrams

Many zero bigram in corpus but maybe needed in translations

linear interpolation

Translation Model

(i) a model of the sentence-aligned source–target training corpus

(ii) a method for computing the probability that S and T are equivalent using that model

Translation Model Example

Example

Word Alignment

Simple Word Alignment

Expectation-Maximisation (EM) algorithm

Expectation-Maximisation (EM) algorithm

MT Evaluation

• How can we measure MT quality?

• How can we compare MT engines?

• How can we measure progress in MT development?

• Adequacy: Does the output convey the same meaning as the input sentence?

Is part of the message lost, added, or distorted?

• Fluency: Is the output good fluent English?

This involves both grammatical correctness and idiomatic word choices.

What do We Expect from MT?

• adequacy & informativeness (preserve meaning)

• fluency & grammaticality (translation needs to be natural)

• acceptance (for its task)

Task-specific evaluation

• browsing quality: Is the translation understandable in itscontext?

• post-editing quality: How many edit operations are required to turn it into a good translation?

• publishing quality: How many human interventions arenecessary to make the entire document ready for printing?

Evaluation is Difficult!

• I What is the best translation? (language variation!)

• I Subjective aspects (What is “fluent”? Clarity? Style?)

• I What is “grammatical”?

• I What is “adequate”? (Is it possible to be adequate?)

MT evaluation

Manual Evaluation

• ask actual users to rate translations

• statistics over user responses

• separate evaluations of adequacy & fluency

• requires guidelines

• task-specific evaluation

Automatic Evaluation

• compare to reference translations

• approximations by measuring overlaps

• strong bias but useful for rapid development

Fluency and Adequacy: Scales

Manual MT evaluation: What are the problems?

• need volunteers (every time we want to evaluate)

• expensive evaluation!

• subjective measures & disagreement between annotators

Automatic Evaluation: BLEU-score

• introduced in 2002 by Papineni et al

• desperately needed by rapid MT development

• quickly adapted by statistical MT community

• created a boom in MT research/experiments

• Many MT papers report only BLEU scores and don’t even look at the

translations

BLEU-score

the closer a machine translation is to a professional human translation

the better it is

Definition

•Pn: for each pair of candidate and reference sentences.

• This score represents the proportion of n-word sequences in the candidate translation which also occur in the reference translation.

• Koehn, Philipp. Statistical machine translation. Cambridge University Press, 2009.

• Arnold, D., et al. "Machine translation: An introductory guide. NCC Blackwell." (1994).

• https://www.slideshare.net/rushdishams/types-of-machine-translation

• https://en.wikipedia.org/wiki/Machine_translation

• Brown, Peter F., et al. "A statistical approach to machine translation." Computational linguistics 16.2 (1990): 79-85.

• Hearne, Mary, and Andy Way. "Statistical machine translation: a guide for linguists and translators." Language and Linguistics Compass 5.5 (2011): 205-226.

Question?