Information Dynamics in Language : English-Hindi

Anusaaraka

Akshar Bharati

LTRC, IIIT, Hyderabad

dipti@iiit.ac.in

(20-04-09)

u3Ld

Outline

Anusaaraka – What it is ?

Anusaaraka – How does it work ?

Information dynamics in language

English – From Paninian perspective

Machine Translation

Anusaaraka – An alternative approach

Anusaaraka Goals

Anusaaraka Philosophy

Summary – What Anusaaraka is

What is Anusaaraka?

Software that translates English text to Hindi.

Fusion of traditional Indian shastras and modern technology.

Collaborative endeavour of CIF, IIIT – Hyderabad and University of Hyderabad (Department of Sanskrit Studies).

How does Anusaaraka work?

User types in the text that needs to be translated.

Machine gives output (i.e. translation).

Option to view step-by-step translation.

Information Dynamics in Language (1/४4)

Languages encode information

cuuhe maarate haiM kutte

rats kill dogs

Hindi sentence is ambiguous

Possible interpretations :

Dogs kill rats

Rats kill dogs

However,

Information Dynamics in Language (2/4)

Ambiguity in Hindi is resolved if,

cuuhe maarate haiM kuttoM ko

rats kill dogs acc

English has information in positions Hindi in morphemes

Languages encode information differently

Information Dynamics in Language (3/4)

English pronouns he, she, it

Hindi vaha

He is going to Delhi vaha dilli jA rahaa hai

She is going to Delhi vaha dillii jA rahii hai

It broke vaha tuta ??

Information does not always map fully from one language into another. Conceptual worlds may be different.

Information Dynamics in Language (4/4)

This chair has been sat on

This chair has been used for sitting

X sat on this chair, and it is known

Language encodes information partially

English from Paninian View Point

ा�An Example:

Panini's 'sutra'

सु� सु�प्� तिङन्म्� प्दम्�

states

प्रा�तिप्दिदक+सु�प्�= सु�बन् प्दNom base+nom inflection=nominal word form

धा��+तिङ� =तिङन्verb root+verbal inflections=finite verb form

Therefore, take the following Hindi example:

रा� रा�म् फल खा�� है� Ram eats fruits

रा�म्+ ० फल+ ० खा�+�_है�है�

रा�म् ने� फल खा�या� Ram ate a fruit

रा�म्+ ने� फल+ ० खा�+या�

English from Paninian View Poinट

Interrogatives in English

To whom did you give the book ? who+to_m do+past you+0 give the book+0

Alternatively

Who did you give the book to ?Who do+past you+0 give the book+o to

Notionसुs of NP and PP are essential to Explain English structures where NP=प्प्रा�तिप्दिदक, PP= सु�बन् (प्द)

to+who = सु�बन्

Translation

Translation involves Transfer of information from one language

to anotherThis generates tension between

Faithfulness to the source Readability (naturalness) in the target

Translators normally sacrifice faithfulness in favour of readability

Machine Translation

Challenges and Problems

Language codes information only partially Tension between BREVITY and PRECISION Brevity wins leading to inherent ambiguity at

different levels

Ambiguity in Language

Can be at the structural level

Can be at the lexical level

Structural Ambiguity

Time flies like an arrow

Possible parses

1. Time flies like an arrow (time goes fast)

2. Time flies like an arrow (time-flies have a liking for an arrow)

3. Time flies like an arrow (time the flies just like you time the arrows) -flies are like an arrow

Lexical Ambiguity (1/3)

Can be

Complete bank banks / banked banks banking (river) bank banks / banked banks banking (money)

Partial lie (not speak truth) lie lied lying lie (rest horizontally) lie lay lying

Lexical Ambiguity (2/3)

Shelve

1. Shelve the books

Put the books on the shelf

2. The Institute has shelved the idea at least until next

year

Postponed the idea till the next year

Function words (1/2)

He bought a shirt with tiny collars.

usane chote kOlaroM vaalii kamiiza khariidii

He washed a shirt with soap.

usane saabuna se kamiiza dhoii

PP attachment is governing the choice of postposition in

Hindi

Function words (2/2)

Ram is sitting in the garden

raama bagiice meM baiThaa haiRam is running in the garden

raama bagice meM dODza rahaa hai

Verb root is governing the choice of TAM

Information Flow and Ambiguity

1. He scratched a figure on the rock (engrave)

2. She scratched the figure on the rock (scrape)

Human beings use

World knowledge

Context

Cultural knowledge and

Language conventions

To resolve ambiguities. Can we provide all this knowledge to the machine ?

Machine Translation: Current Trends

Techniques being used: Statistical

Statistical methods: Inherent limitation

Can never give a 100% reliable system

End user can never be sure about the Correctness.

Current MT systems CAN NOT give a system for

users who want to ACCESS a text in other languages

Anusaaraka

An Incremental Machine Translation

Layered output

First layer a Language ACCESOR

Successive layers more and more close

to MT

What is an Accessor ?

Gist Terminal is a concrete example of SCRIPT ACCESSOR

(Developed by IIT Kanpur, and marketed by C-DAC)

One can access any text in any Indian script

through

-- enhanced Devanagari script.

For example, the following two Telugu words

Can be displayed in enhanced Devanagari script.

Salient Features Faithful representation

Reversibility

Anusaaraka tries to generalise and apply this

philosophy to the problem of language

conversion which is several order more

complex

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

Anusaaaraka follows the approach of gradually reducing the distance

Anusaaraka Solutions

Transliteration

Padasutra for Vocabulary substitution

WSD for word level ambiguity

Transfer Grammar

Transfer Grammar

Eng : This chair has been sat on

Transli : दिदसु चे�यारा है�ज़ ब ने सु�ट आने

Lexical substitution : याहै याहै क� सु" ब�ठा� जा� चे�क� है� प्रा

Transfer grammar :ि्िि्िा� इसु क� कक� सु" प्रा ब�ठा� जा� चे�क� है�

Padasutra (1/2)

Get the core meaning of a polysemous word State it in a formulaic formठातिहैसुThis appears in the first Write notes to show the relatedness of various senses The user can refer to it if required

Padasutra (2/2)English verb 'have' 1. She has tea in the morning

vaha(nom) subaha caaya piitii hai (drink)

2. She has bread in the morning

vaha(nom) subaha breda khaatii hai (eat)

3. She has fever

usako bukhaara hai (be)

4. She has my book

usake paasa merii pustaka hai (posses+be)

5. She has three children

usake tiina bacce haiM

Word Sense Disambiguation (WSD)

WSD is

Automatically selecting the appropriate sense in a given context

Requires linguistic Resources and Tools

Linguistic resources : dictionaries, thesauri, hand crafted rules etc Linguistic tools : POS tagger, Parser, MWE Identifier etc

WSD : Possible Solutions

Two major approachesManually crafted rules

Costly Fragile

Machine Learning/Statistical

Anusaaraka Solution to WSD (1/4)

Major bottleneck

Requires large number of disambiguation rules

Anusaaraka combines statistically generated rules with manually created rules

WSD rules can be revised/added over a period of time

Simplify the method for the above

Involve large number of people to prepare rules

Anusaarak uses 'clips' an Expert System Shell for developing rules

Anusaaraka Solution (2/4)

Divide the problem into small bite size with considerable time

Bite size – 4 pagesTime – Two years

Relevant in Indian conditions as we have large manpower

Anusaaraka Solution (3/4)

Use manually craftd rules for WSD

Which means developing WSD rules for approximately 10,000 words

Handling MWE in lakhs

Anusaarka Solution (4/4)

Use Cambridge Advanced Learners dictionary for distributing words to the rule developers

The dictionary has Approx 1600 pagesAllot 4 pages to one person

1600/4=400

Anusaaraka Goals

Provide an open source usable system to the users

The system should facilitate accessing another language

Show the usability of Indian traditional grammar system in the modern context Facilitate users to become developers

Anusaaraka Philosophy

No Loss of Information

No efforts should go wasted

Users contribute towards the

development

Anusaaraka is An application of concepts from Panini's

Ashtadhyayi to contemporary problems

• pravitti nimitta

• sannidhi (proximity)

• yogyataa (qualification)

• aakaaMkshaa (expectation)

• kaarakas (role-relations)

• etc

Anusaaraka is A tool for overcoming language barriers An application of concepts from Panini's ashtadhyayi to contemporary problems.

An exploration of the information dynamics in language

A better approach for building Machine Translation systems

A Workbench for NLP students An opportunity for the masses to be IT contributors rather than mere IT consumers

Paninian Grammar Inspired Information Dynamics

Basics Core IdeaSyntax

Vocabulary

EnglishGrammarfrom Paninianview point

WSD(Incommensurability)

Concrete ExamplesAnusaaraka cum Machine Translation System

a. Scientific Aspect

b. Engineering AspectBasics

a) Evolutionary Approachb) Graceful Degradationc) Providing Practical Alternatives

Anusaaraka cumMachine Transaltion

Core IdeaLayered Output

Smart UserInterface

c. Social AspectBasics

Gitaa) yajna

b) tyena tyaktena bhunjithaa

Temple of Learning

Core Idea

InspiredInspired

inspired

Open Source

Users are not mere consumersbut can also participate in the development

Bringing out hidden talents

What Next ?

Developing Anusaaraka as an NLP Workbench

Ideas are welcome on how to proceed on this

The Following discussion will focus on this