Creation of a Russian-English Translation Program

Karen Shiells

Purpose

Object-oriented approach Interactive machine translation Designed for aid, not independent translation Explore algorithms used in machine translation Identify grammatical obstacles to translation Create a base to expand later

Scope of Study

Machine translation is and will be imperfect Modern translation uses statistical methods Project is limited to:

Separating base words from morphological endings Constructing syntax trees from source text Generating simple English output from tree Identifying words already known to the program

Other Research

Part-of-speech tagging: Uses probability to identify parts of speech Applied to unknown words and structures Complex labeling systems, beyond conventional

Translation algorithms: Massive dictionaries store words and information Aided by verb categorization Omit unknown words and translate without Usually comprehensible, but require human revision

Old Methods

Direct Translation First method Rearranges sentences without parsing Based on rules of transfer for specific languages

Interlingua From era of international languages Uses one representation as an intermediary Intermediary is usually a constructed language Easier to add language pairs

Syntactic Transfer

Similar to interlingua Generates syntax tree using specific parser Rearranges tree to fit target structure Uses specific generation method to form output Entire algorithm specific to one language pair Best quality translations Relatively new Not as common in commercial software

Alternative Structures

Valency Stores number of complements for each word Type of complements not specified Occupies less space in dictionary

Phrase-Structure Representation Most familiar: noun phrase, verb phrase, etc. Breaks sentence into superstructures Puts terminal symbols only in leaves Non-terminal symbols for branches

Dependency Trees

Uses words as nodes, not just leaves Examples:

Verb dependent on subject Objects dependent on verb Adjectives dependent on nouns Prepositions vary by type of prepositional phrase

Easier to verify agreement between words Occupies less space

Object Orientation

Object-oriented approach allows more flexibility Endings, cases, and declensions are classes Fewer hard-coded rules Methods for locating dependents are in classes Modular design allows gradual changes

Changes in lexical analysis do not affect parsing Changes in dictionary do not affect translation

Verb Typing

Divides verbs into categories, for example: Transitive Intransitive Directional or Non-directional motion

Condenses structure storage Dictionary stores only type of a verb Particular structures taken from general Code can apply to general structures, not specific

Dictionary

Open, save, add, remove, and search functions Stores:

Russian nominative English nominatives Part of speech Noun/pronoun attributes Verb types

Translator

Uses transliteration for ease of testing Can be easily converted to Unicode Cyrillic Debugging output to terminal window

Results

Subject, verb, direct object translated Subject is first nominative Verb matched by gender, number, and person Direct object is first accusative

Adjectives matched to nouns Matched by case, number, and gender Word order not considered

Word order should be accounted for, but aren't Adjectives to nearest, not matching Prepositional objects should be nearby

Conclusions Part-of-speech guessing could be added easily

When a subordinate is not found, add to list For each unmatched word, prompt user Allow selection between subordinates not found

Verb typing would be harder, but helpful Restricting complements makes more precise More efficient, not searching for all possible Prepositions could be associated with nouns

Even in inflecting languages, word order matters Subordinates should be located by proximity Multiple functions use the same inflections

Bibliography

Allen, James. Natural Language Understanding. New York: Benjamin/Cummings Publishing Company, 1995.

Arnold, Doug, Lorna Balkan, Siety Meijer, R. Lee Humphreys, and Louisa Sandler. Machine Translation: An Introductory Guide. London: NCC Blackwell, 1994. Available Online: http://www.essex.ac.uk/linguistics/clmt/MTbook/PostScript.

Barber, Charles. The English Language: A Historical Introduction. Cambridge: Cambridge University Press, 1993.

Beard, Robert. “Russian: An Interactive On-Line Reference Grammar”. November 1, 2005. Available Online: http://www.alphadictionary.com/rusgrammar/.

Comrie, Bernard, ed. The World's Major Languages. Oxford: Oxford University Press, 1990.

Hutchins, John and Harold Somers. An Introduction to Machine Translation. London: Academic Press, 1992. Available Online: http://ourworld.compuserve.com/hompages/WJHutchins/IntroMT-TOC.htm.