machine translation across indian languages dipti misra sharma ltrc, iiit hyderabad patiala...

Machine Translation across Indian Languages Dipti Misra Sharma LTRC, IIIT Hyderabad Patiala 15-11-2013

Outline Introduction Information Dynamics in language Machine Translation (MT) Approaches to MT Practical MT systems Challenges in MT Ambiguities Syntactic differences in L1 an L2 MT efforts in India Sampark : IL to IL MT systems Objective Design Issues Conclusions

Introduction Natural Language Processing (NLP) involves Processing information contained in natural languages Natural as opposed to formal/artificial Formal languages : Programming languages, logic, mathematics etc Artificial : Esperanto

Natural Language Processing (NLP) Helps in Communication between Man-machine Question answering systems, eg interactive railway reservation Man man Machine translation

Communication Transfer of information from one to the other Language is a means of communication Therefore, one can say It encodes what is communicated We apply the processes of Analysis (decoding) for understanding Synthesis (encoding) for expression (speaking)

What do we communicate ? Information Spain delivered a football masterclass at Euro 2012 Intention Emphasis/focus Euro 2012 bagged/won by Spain Spain bags Euro 2012 Introduces variation

How do we communicate ? We use linguistic elements such as Words (country, park, the, is, Bandipur, of, as, and, considered, National, a, spot, beautiful, tourist, life, in, best, wild, sanctuaries, the, one) Arrangement of the words (Sentences) Words are related to each-other to provide the composite meaning (Bandipur National park is a beautiful tourist spot and considered as one of the best wild life sanctuaries in the country)

How do we communicate ? Contd.. Arrangement of sentences (Discourse) Sentences or parts of sentences are related to each other to provide a cohesive meaning *Considered as one of the best wild life sanctuaries in the country. It is a national park covering an area of about 874 km. Bandipur National park is a beautiful tourist spot. Bandipur National park is a beautiful tourist spot and considered as one of the best wild life sanctuaries in the country. It is a national park covering an area of about 874 km Languages differ in the way they organise information in these entities All of these interact in the organisation of information

Information Dynamics in Language (1/4) Languages encode information Hindi: cuuhe maarate haiM kutte ' rat-pl' 'kill-hab' 'pres-pl' 'dog-pl' rats kill dogs Hindi sentence is ambiguous Possible interpretations Dogs kill rats Rats kill dogs However, English sentence is not ambiguous

Information Dynamics in Language (2/4) Ambiguity in Hindi is resolved if, cuuhe maarate haiM kuttoM ko rats kill-hab pres-pl dogs-obl acc Hindi encodes information in morphemes English encodes information in positions Languages encode information differently

English does not explicitly mark accusative case (except in pronouns) no morpheme No lexical item/morpheme for yes no questions (Eng: Is he coming ? Hindi : kyaa vah aa rahaa hai?) Position plays an important role in encoding information in English Subject is sacrosanct Hindi encodes information morphologically

Information Dynamics in Language (3/4) Another example, This chair has been sat on The chair has been used for sitting Someone sat on this chair, and it is known The sentence does not mention someone Languages encode information partially

Information Dynamics in Language (4/4) English pronouns he, she, it Hindi pronounvaha He is going to Delhi ==> vaha dilli jaa rahaa hai She is going to Delhi ==> vaha dillii jaa rahii hai It broke ==> vaha TuuTa ?? Information does not always map fully from one language into another Conceptual worlds may be different Gender Information

Information in Language Languages encode information differently Languages code information only partially Tension between BREVITY and PRECISION

Human beings use World knowledge Context (both linguistic and extra-linguistic) Cultural knowledge and Language conventions to resolve ambiguities Can all this knowledge be provided to the machine ?

Languages differ Script (For written language) Vocabulary Grammar These differences can be considered as a measure of language distance

Language Distance Script -------------- Vocabulary----------Grammar Urdu-> Hindi Telugu -> Hindi Telugu->Hindi English -> Hindi English-> Hindi English->Hindi

Machine Translatoion Machine translation aims at automatic translation of a text in source language to a text in the target language. Mohan gave Hari a book -> Mohan ne Hari ko kitAba dI

Machine Translation Let us view MT as a problem of Language encoding (source) - analysis Decoding (target) - synthesis

English to Hindi : An Example SL (Eng) sentence : I met a boy who plays cricket with you everyday Mapped to TL(Hin) : I a boy met who everyday with you cricket plays TL synthesis : mEM eka laDake se milA jo roza tumhAre sAtha kriketa khelatA hE OR mEM roza tumhAre sAtha kriketa khelanevAle eka laDake se milA OR meM eka Ese laDake se milA jo roza tumhAre sAtha kriketa khelatA hE

Machine Translation : Challenges Languages encode information differently Language codes information only partially Tension between BREVITY and PRECISION Brevity wins leading to inherent ambiguity at different levels

Linguistic Issues in MT (1/2) Look at the word 'plot' in the following examples (a) The plot having rocks and boulders is not good. (b) The plot having twists and turns is interesting. 'plot' in (a) means 'a piece of land' and in (b) 'an outline of the events in a story'

Linguistic Issues in MT (2/2) Ambiguity in Language Lexical level Sentence level Structural differences between SL and TL

Lexical ambiguity Lexical ambiguity can be both for Content words nouns, verbs etc Function words prepositions, TAMs etc Content words ambiguity is of two types Homonymy Polysemy

Homonymy A word has two or more unrelated senses Example : I was walking on the bank (river-bank) I deposited the money in the bank (money-bank)

Polsysemy 'Act', an English noun 1. It was a kind act to help the blind man across the road (kArya) 2. The hero died in the Act four, scene three (aMka) 3. Don't take her seriously, its all an act (aBinaya) 4. The parliament has passed an Act (dhArA)

Function words can also pose problems (1/5) Prepositions English prepositions in the target language Tense Aspect Modality (TAM) Lexical correspondence of TAM

Function words can also pose problems (2/5) Function words can also be ambiguous For example English preposition 'in' (a) I met him in the garden mEM usase bagIce meM milA (b) I met him in the morning mEM usase subaha 0 milA 'Ambiguity' here refers to the 'appropriate correspondence' in the target language.

Function words can also pose problems(3/5) He bought a shirt with tiny collars. usane chote kOlaroM vAlI kamIza kharIdI he tiny collars with shirt bought with gets translated as vAlI in hindi He washed a shirt with soap. usane sAbuna se kamIza dhoI he soap with shirt washed with gets translated as se.

Function words can also pose problems (4/5) TAM Markers m ark tense, aspect and modality Consist of inflections and/or auxiliary verbs in Hindi An important source of information Narrow down the meaning of a verb (eg. lied, lay)

Function words can also pose problems (5/5) English Simple Past vs Habitual' 1a. He stayed in the guest house during his visit to our University in Jan (rahA) 1b. He stayed in the guest house whenever he visited us (rahatA thA) 2a. He went to the school just now (gayA) 2b. He went to the school everyday (jAtA thA)

Sentence level ambiguity o I met the girl in the store + Possible readings a) I met the girl who works in the store b) I met the girl while I was in the store o Time flies like an arrow. + Possible parses: a) Time flies like an arrow (N V Prep Det N) b) Time flies like an arrow (N N V Det N) c) Time flies like an arrow (V N Prep Det N) (flies are like an arrow) d) Time flies like an arrow (V N Prep Det N) (manner of timing)

Differences in SL and TL Lexical level (a) One word may translate into different words in different contexts (WSD) English 'plot' zamiin, kathanak (b) A SL word may not have a corresponding word in the TL (Gaps) English 'reads' in 'This book reads very well' (d) Pronouns across Indian languages Hindi 'vaha' Telugu 'adi', 'atanu', 'aame'

Differences in SL and TL Structural differences (a) word order (English Hindi) (b) nominal modification (Hindi Tamil, Telugu etc) (i) relative clause vs relative participles Telugu 'nenu tinnina camcaa' Hindi : *meraa khaayaa cammaca Maine jis cammaca se khaayaa hai vah cammac (ii) missing copula (Hindi Telugu, Bengali, Tamil etc) Telugu : raamudu mancivaadu Hindi : Ram acchaa ladakaa hai

Human beings use World Knowledge Context Cultural knowledge and Language conventions To resolve ambiguities and interpret meaning

What to do for the machine ? Challenging problem!!! Providing all the knowledge may: - take too much of time and effort - be difficult/become complex - not be possible (world knowledge acquired from experience) Therefore, Break the problem into smaller problems Choose the solution as per the nature of problem Build language resources to the extent possible and continue to add to it Engineer knowledge efficiently

Approaches to MT (1/2) Rule-based or Transfer based Uses linguistic rules to map SL and TL, such as Maps grammatical structures Disambiguation rules Knowledge-based Extensive knowledge of the domain Concepts in the language Ability to reason

Approaches to MT (2/2) Example-based Mapping is based on stored example translations Translation memory based Uses phrases/words from earlier translation as examples Statistical Does not formulate explicit linguistic knowledge Develops rules based on probabilities Hybrid Mixes two or more techniques

A Glance at MT Efforts in India (1/4) Domain Specific Mantra system (C-DAC, Pune) Translation of govt. appointment letters Uses Tree Adjoining Grammar Public health compaign documents Angla Bharati approach (C-DAC Noida & IIT Kanpur)

A Glance at MT Efforts in India (2/4) Application Specific Matra (Human aided MT) (NCST,now C-DAC, Mumbai) General Purpose (not yet in use) Angla Bharati approach (IIT Kanpur ) UNL based MT (IIT Bombay) Shiva: EBMT (IIIT Hyderabad/IISc Bangalore) Shakti: English-Hindi MT system (IIIT Hyderabad)

MT Efforts in India (3/4) Major Government funded MT projects in consortium mode Indian Language to Indian Language Machine Translation (ILMT) (Lead Institute - IIIT, Hyderabad) English to Indian Language Machine Translation Mantra, Shakti etc (Lead inst - C-DAC, Pune) Anglabharati (Lead inst IIT, Kanpur) Sanskrit to Hindi MT System (Lead Inst University of Hyderabad)

MT Efforts in India (4/4) Anusaaraka : Language Accesspr cum MT System (IIIT, Hyderabad, Chinmaya Shodh Sansthan)

Our Focus Sampark : Indian Language to Indian Language MT systems

Sampark : Indian Language to Indian Language MT Systems Consortium mode project Funded by DeiTY 11 Partiicpating Institutes Nine language pairs 18 Systems

Participating institutions IIIT, Hyderabad (Lead institute) University of Hyderabad IIT, Bombay IIT, Kharagpur AUKBC, Chennai Jadavpur University, Kolkata Tamil University, Thanjavur IIIT, Trivandrum IIIT, Allahabad IISc, Bangalore CDAC, Noida

Objectives Develop general purpose MT systems from one IL to another for 9 language pairs Bidirectional Deliver domain specific versions of the MT systems. Domains are: Tourism and pilgrimage One additional domain (health/agriculture, box office reviews, electronic gadgets instruction manuals, recipes, cricket reports) By-products basic tools and lexical resources for Indian languages: POS taggers, chunkers, morph analysers, shallow parsers, NERs, parsers etc. Bidirectional bilingual dictionaries, annotated corpora, etc.

Language Pairs (Bidirectional) Tamil-Hindi Telugu-Hindi Marathi-Hindi Bengali-Hindi Tamil-Telugu Urdu-Hindi Kannada-Hindi Punjabi-Hindi Malayalam-Tamil

User Scenario Web based system for tourism/ pilgrimage domain. A common traveler/tourist/piligrim to access info in his language. Access to selected Government portals in agriculture/health Automatic MT in domain General purpose web based translation Potential to attach to major search engines such as Google, Yahoo, Microsoft, Web-duniya

Design and Approach Largely transfer based Analysis, Transfer, Generate Modular (module could be Pipeline architecture Hybrid some modules statistical, some rule based Analysis : Shallow parser No deep parsing in the first phase

Approach Largely transfer based Analysis, Transfer, Generate Modular Modules could be statistical or rule based depending on the nature of problem (Hybrid) Pipeline architecture Analysis : Shallow parsing followed by a simple parser

Design o Design decisions based on - the commonality in Indian languages - easy to extend to other languages o Phase the development - Phase 1 o Analysis at sentence level o Shallow parser o Simple parser o Transfer : map lexicon, structures, script o Generate the target

Design Contd Phase 2 Extend the analysis to discourse level Anaphora resolution Relations between clauses (discourse connectives) Word Sense Disambiguation (WSD) Named Entity Recognition (NER) Multi Word Expressions (MWE) Explore SMT for transfer rules

Transfer based MT Source Sentence Source Analysis Analysis Analysis in Target Language Target Sentence Transfer Generation

Form (Input sentence/text) Meaning Analysis Form Generation L1 Various types of linguistic information helps in arriving from form to meaning It is complex. Modularization helps in simplifying it.

Modularize Word Structure In context Morph Analyser Syntactic What is functions as Semantic What it means (POS tagger) (WSD) Relations between words Local (local word grouping,/ chunking) Non-local (Subject,object/karaka)

Form (Input sentence/text) Meaning Analysis Form Generation Semantic analysis POS Chunking parsing Morph Analysis Formal semantics All this information is implicit in language. How to make it explicit? Build resources Dictionaries, Verb frames, Treebanks

Sampark Architecture

Details Standards Annotation standards POS and Chunk Input output of each module Representation - SSF Data format Dictionaries Emphasis on proper software engineering Development environment Dashboard Blackboard architecture CVS for version control etc.

Machine Learning: Separating engines from language data Module for Task (T) Sentence in Language (L) Training data (lang. L) Engine for task T Out Manual Correction

Horizontal Tasks H1 POS Tagging & Chunking engine H2 Morph analyser engine H3 Generator engine H4 Lexical disambiguation engine H5 Named entity engine H6 Dictionary standards H7 Corpora annotation standards H8 Evaluation of output (comprehensibility) H9 Testing & integration

Vertical Tasks for Each Language V1 POS tagger & chunker V2 Morph analyzer V3 Generator V4 Named entity recognizer V5 Bilingual dictionary bidirectional V6 Transfer grammar V7 Annotated corpus V8 Evaluation V9 Co-ordination

An Example : Hindi to Panjabi System 1500 . . . . . 1500

Panjabi to Hindi . . . 23 1931 , . . 23 1931 ,

Panjabi to Hindi (NER) . (WSD) (Agreement) . (word generation) . 23 1931 , (function word substitution) . .

Evaluation Testing, system integration, and evaluation team Involvement of industry Regular In-house subjective evaluation Third party evaluation on system submission

Achievements of ILMT Project Phase I 18 MT systems built among Indian languages Shallow parser for all 9 Indian languages Lexical resources for all 9 languages Largely built from scratch Developed standards for all stages Developed open architecture

Achievements -Deployment Deployed and running over web 8 systems (sampark.org.in ) Others deployed over ILMT test site 4 more ready to go to Sampark soon Rest are being evaluated and tested internally (require a few more months to go to Sampark site after reaching quality levels) Constant qualilty improvement going on for various existing modules New modules are under testing and would be soon integrated

Future Tasks Enhance the quality of MT output Enhancing dictionaries Increasing coverage of grammar Adding new technology to ILMT systems Full sentence parsing Discourse processing - anaphora Target some users

Some Possibilities Possible tie up with search engines companies Possible tie up with content companies such as - Dainik Jagran, Web duniya, Rediff, Yahoo Identify translation bureaus and agencies Build MT workbench for their use, their domains, etc. Poised for major public impact with a unique technology.

Future Systems Add language pairs Gujrati Hindi Kashmiri Hindi Manipuri Hindi Oriya Hindi Etc

CONCLUSION Developing MT systems, though a challenging task, is a useful effort particularly in the multilingual context of India

machine translation across indian languages dipti misra sharma ltrc, iiit hyderabad patiala...

Documents

information hindi

processing information

information spain

encoding information

country slide

ambiguous slide

esperanto slide

variation slide