Saurabh Chanderiya (07005004)Abhimanyu Dhamija (07005024)

E K Venkatesh (07005031)G Hrudil (07005032)

B Vinod Kumar (07d05018)

Guide: Prof. Pushpak Bhattacharya

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Outline What is Natural Language Generation?

Motivation

Stages in NLG

Applications of NLG

Evaluation Techniques

Conclusion

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What is Natural Language Generation? Natural Language Generation (NLG) is the subfield of

artificial intelligence and computational linguistics that focuses on computer systems that can produceunderstandable texts in English or other human languages

[Reiter and Dale, 2000]

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What is Natural Language Generation? Convert computer based representation into natural

language representation (opposite of NLU)

Machine representation comprises of some form of computerized data

Examples: A database of daily temperature values in a city

An ontology

A collection of fairy tales

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Natural Language Text

NLGData/ Machine-representation

NLU

Key Elements in NL Generation Many choices available – an NLG system needs to

choose the most appropriate one

Example: Denoting value-change

“the temperature rose” – increase in value

“the temperature plummeted” – drastic increase in value

“the rain got heavier” – again increase in value, but different context

[Wikipedia]

Meeting the communication goals – so that the generated text is understandable to the target reader

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Motivating Example1

Suppose you are asked to write an article on IIT Bombay. How do you proceed?

Step 1: What all should I write about? How should I organize it? History, Students, Professors, Gymkhana, Mood Indigo …

Start with description of gymkhana or history …

Step 2: What should my style be? Editorial, Prose, Poetry …

Simple words …

Step 3: Pen it down

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Motivating Example2

We have just identified the key stages in Natural Language Generation

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Motivating Example3

We have just identified the key stages in Natural Language Generation

Step 1: What all should I write about? How should I organize it?

History, Students, Professors, Gymkhana, Mood Indigo …

Start with description of gymkhana or history …

TEXT PLANNING

(Content Determination and Document Structuring)

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Motivating Example4

We have just identified the key stages in Natural Language Generation

Step 2: What should my style be?

Editorial, Prose, Poetry …

Simple words …

MICROPLANNING

(Lexical Choice, Referring Expression Generation, Aggregation)

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Motivating Example5

We have just identified the key stages in Natural Language Generation

Step 3: Pen it down

REALIZATION

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NLG Systems Architecture

Control Data

RealizationDocument Planning

Micro -planning

Input Data

Output Data

Content Determination

Document Structuring

Lexical Choice

Referring Expressions

Aggregation

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Stages in NLG The following different stages of Natural Language

Generation can be identified:

Content Determination

Document Structuring

Lexical Choice

Referring Expression Generation

Aggregation

Realization

Each of these is considered in detail in the next few slides

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Content Determination1

Deciding what information to mention in the text

Example:

[Wikipedia]

NLG system to summarize information about sick babies has the following information:

The baby is being given morphine via an IV drop

The baby's heart rate shows bradycardias (temporary drops)

The baby's temperature is normal

The baby is crying

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Content Determination2

Factors affecting the decision could be

Communicative goal – the purpose of the text and the reader

A diagnosing doctor would be interested in heart rate while a parent would want to know if the baby is crying or not

Size and level of detail

A formal report about the patient vs. an SMS to the doctor

How unusual the information is

Is it important to mention that the baby’s temperature is normal?

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Content Determination3

Techniques employed

Schemas – predefined templates which explicitly specify what information is to be included Based on Rhetorical Predicates

Rhetorical predicates specify the “role” that is played by each utterance in the text

Example: Mary has a pink coat

Attributive

Other rhetorical predicates: Particular illustration, evidence, inference etc.

[McKeown, 1985]

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Content Determination4

Example Schema using Rhetorical Predicates

Identification Schema (for providing definitions)

[McKeown, 1985]

Identification (class & attribute)

Attributive

Particular Illustration

Sample text generated from this schema could be

Mumbai is an important economic region in Maharashtra. There are many textile mills in Mumbai. Bombay Dyeing is among the noteworthy textile mills.

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Content Determination5

Explicit Reasoning Approaches

Example: Plot generation using case based reasoning

[B. D´ıaz-Agudo et. al, 2004]

Case based reasoning characterized by: retrieve, reuse, revise, retain

Build cases from a set of stories – similar to identifying features that constitute the story

Ontology for the fairy tale world

Accept query from user regarding features of the new plot to be generated

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Content Determination6

Example: Plot generation using case based reasoning (contd.)

Retrieve similar case – similarity calculated on the basis of distance in the ontology

Resolve dependencies – ask user for further input if needed

Generate plot

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Content Determination7

Sample run:

Query: “princess, murder, interdiction, interdiction violated, competition, test of hero”

Story number 113 (Swan Geese) returned based on similarity

Perform substitutions

Generate plot

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Document Structuring1

Decide the order and grouping of sentences in a generated text

Example:1. John went to the shop.

2. John bought an apple.

Now consider:1. John bought an apple.

2. John went to the shop.

The first case seems more coherent than the second. Thus, sentence structuring is important.

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Document Structuring2

Algorithms Schema based approach

Corpus based Approach [M Lapata, 2003]P(S1 … Sn) = P(S1) * P(S2|S1) * P(S3|S1,S2) * … *P(Sn|S1 … Sn-1)

(assuming dependence only on previous sentence)

P(S1 … Sn) = P(S1) * P(S2|S1) * P(S3|S2) * … * P(Sn|Sn-1)

(using features to represent sentences)

P(S1 … Sn) = P((a<i,1>, a<i,2>, … , a<i,n>) | (a<i-1,1>, … a<i-1,m>))

(assuming independence of features and approximating P(S<i>|S<i-1>) from the Cartesian product S<i> x S<i-1>)

P(S<i>|S<i-1>) = π {P(a<i,j>|a<i-1,k>)} where jεS<i> and k ε S<i-1>

(estimate prob. using counts, construct directed weighted graph (sentences as nodes and probabilities as edge weights) and obtain approximate solution)

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Aggregation1

Aggregation is a subtask of Natural language generation, which involves merging syntactic constituents (such as sentences and phrases) together

Example: John went to the shop. John bought an apple. “John went to the shop and bought an apple.”

Could be syntactic or conceptual Example of conceptual: replacing “Saturday and Sunday”

by “weekend” Aggregation algorithms must do two things:

Decide when two constituents should be aggregated Decide how two constituents should be aggregated, and

create the aggregated structure

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Post-editing2

Identity between different word-groups

Lemma identity: two different words belong to the same inflectional paradigm

Form identity: two words have the same spelling/ sound and are lemma-identical

Co-referentiality: two words/constituents denote the same entity or entities in the external context, i.e. have the same reference

[Karin Harbusch et. al, 2009]

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[Karin Harbusch et. al, 2009]

Lexical choice1

Lexical choice involves choosing the content words (nouns, verbs, adjectives, adverbs) in a generated text.

The simplest type of lexical choice involves mapping a domain concept to a word.

Lexical choice modules must be informed by linguistic knowledge of how the system's input data maps onto words. This is a question of semantics, but it is also influenced by syntactic factors and pragmatic factors.

3 factors to look for: Genre

People perceive different words differently

How language relates to the non-linguistic world

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Humans’ perception about words3

[Rohit Parikh, 1994]

By evening: has different meaning

Different dialects

Choosing between near-synonymous words

It has been suggested that utility theory be applied to word choice. In other words, if we know (1) the probability of a word’s being correctly interpreted or misinterpreted and (2) the benefit to the user of correct interpretation and the cost of mis-interpretation, then we can compute an overall utility to the user of using the word.

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Referring expression generation1

This the second last stage in natural language generation

This involves creating referring expressions (noun phrases) that identify specific entities to the reader

Example:

He told the tourist that rain was expected tonight in Southern Scotland.

He, the tourist, tonight and Southern Scotland are reference expressions

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Criteria for good referents2

Ideally, a good referring expression should satisfy a number of criteria: Referential success: It should unambiguously identify

the referent to the reader.

Ease of comprehension: The reader should be able to quickly read and understand it.

Computational complexity: The generation algorithm should be fast

No false inferences: The expression should confuse or mislead the reader by suggesting false implications or other pragmatic inferences. [Wikipedia]

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Kinds of Referring Expressions3

Proper noun-noun

Definite Noun Phrases

Spatial

Temporal Reference

Different Algorithmic models Graph-Based Generation of Referring Expressions [Krahmer,

et. al. 2003]

Centering theory uses ranking [Poesio et. al, 2004 ]

Generating Approximate Geographic Descriptions [Turner et. al, 2009]

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Realization1

Realization deals with creating the actual text from the abstract representation

Realization involves three kinds of processing: Syntactic realization – decide order of components, add

function words etc. Example: in English, Subject usually precedes the verb

Morphological realization – compute inflected forms Example: plural(woman) == women

Orthographic realization Capitalization of first letter, punctuations etc.

Realization systems: simplenlg, kpml etc.

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Realization2

SIMPLENLG a simple NLG library for Java for generating grammatically

correct English sentences Sample code:

SPhraseSpec p = nlgFactory.createClause();p.setSubject("Mary");p.setVerb("chase");p.setObject("the monkey");

String output2 = realiser.realiseSentence(p);System.out.println(output2);

Output:“Mary chases the monkey”

[http://code.google.com/p/simplenlg/wiki/Section1]

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Applications of NLG1

Present information in more convenient way

Airline schedule database

Accounting spreadsheet

Automating document production

Doctor writing discharge summaries

Programmer writing code documentation, logic description etc.

In many contexts, human intervention is required to create texts

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Application of NLG with human intervention2 NLG system is used to produce an initial draft of a

document which can be further edited by human author

E.g.

Weather Reporter, which helps meteorologists compose weather forecasts

DRAFTER, which helps technical authors write software manuals

AlethGen, which helps customer-service representatives write response letters to customers

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Application of NLG without human intervention3

Some NLG systems have been developed with the aim of operating as standalone systems.

E.g.

Model Explainer, which generates textual descriptions of classes in an object-oriented software system

LFS, which summarizes statistical data for the general public

PIGLET, which gives hospital patients explanations of information in the patient records.

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Weather Reporter4

Provide retrospective reports of the weather over periods whose duration is one month

Takes large set of numerical data

Produces short texts

E.g. text produced by Weather reporter

The month was cooler and drier than average, with the average number of rain days. The total rain for the year so far is well below average. There was rain on every day for eight days from the 11th to the 18th

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Weather Reporter5

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Weather Reporter6

Data shown is real data collected automatically by meteorological data gathering equipment

Weather Reporter design is based on real input data and a real corpus of human-written texts

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Weather Reporter Example, using the historical data for 1-July-2005, the

software produces Grass pollen levels for Friday have increased from the

moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country. However, in Northern areas, pollen levels will be moderate with values of 4.

In contrast, the actual forecast (written by a human meteorologist) from this data was Pollen counts are expected to remain high at level 6 over most

of Scotland, and even level 7 in the south east. The only relief is in the Northern Isles and far northeast of mainland Scotland with medium levels of pollen count.

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Model Explainer7

Generates textual description of information in models of object-oriented software.

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Model Explainer8

O-O models are usually depicted graphically

Model Explainer is useful as certain kind of information is better communicated textually

E.g.

Via Model Explainer it is clear that a section must be taught by exactly one professor

Clear data especially for people who are not familiar with the notation used in the graphical depiction

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Model Explainer9

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Model Explainer10

It also express relations from the object model in a variety of linguistic contexts

E.g. “teaches”

A professor teaches a course

A section must be taught by a professor

Professor smith does not teach any sections

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Task-Based Evaluation Task-based evaluations measure the impact of

generated texts on end users and typically involve techniques from an application domain such as medicine.

For example, a system which generates summaries of medical data can be evaluated by giving these summaries to doctors, and assessing whether the summaries helps doctors make better decisions.

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Evaluations Based on Human Ratings and Judgments Another way of evaluating an NLG system is to ask

human subjects to rate generated texts on an n-point rating scale

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Unigram PrecisionCandidate: the the the the the the the.Reference 1: The cat is on the mat.Reference 2: There is a cat on the mat. Unigram Precision of above candidate is 7 as 7

candidate words(“the”) occur in reference1. But candidate is not appropriate.

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Modified Unigram precision Count the maximum number of times a word occurs in

any single reference translation.

Clip the total count of each candidate word by its maximum reference count

Add these clipped counts

Divide by the total number of candidate words.

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Modified unigram precisionCandidate: the the the the the the the.

Reference 1: The cat is on the mat.Reference 2: There is a cat on the mat.

Max count of “the” in ref1 is 2.

Total # of candidate words is 7

So, Modified Unigram Precision = 2/7

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Modified n-gram precision All candidate n-gram counts and their corresponding

maximum reference counts are collected.

The candidate counts are clipped by their corresponding reference maximum value.

Add them.

Divide by the total number of candidate n-grams

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Modified n-gram precision A translation using the same words (1-grams) as in the

references tends to satisfy adequacy.

The longer n-gram matches account for fluency.

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Modified n-gram precisionCandidate 1: It is a guide to action which ensures that the

military always obey the commands the party.Candidate 2: It is to insure the troops forever hearing the

activity guidebook that party direct.

Reference 1: It is a guide to action that ensures that the militarywill forever heed Party commands.

Reference 2: It is the guiding principle which guarantees the military forces always being under the command of the Party.

Reference 3: It is the practical guide for the army always to heed directions of the party

Modified Bigram Precision of candidate1 = 8/17

Modified Bigram Precision of candidate2 = 1/13

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Modified n-gram precisionon a multi-sentence

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Modified N-gram Precision : Sentence LengthCandidate: of the

Reference 1: It is a guide to action that ensures that the military will forever heed Party commands.

Reference 2: It is the guiding principle which guarantees the military forces always being under the command of the Party.

Reference 3: It is the practical guide for the army always to heed directions of the party.

Modified Unigram Precision = 2/2

Modified Bigram Precision = 1/1

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Brevity Penality Candidate translations longer than their references are

already penalized by the modified n-gram precision measure: there is no need to penalize them again.

Brevity Penality = 1 if candidate matches a reference. Else it is < 1.

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Effective Reference Length• best match lengths We call the closest reference sentence length to

candidate length the “best match length.”

Effective Reference Length

Sum of all the best match lengths

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Conclusion Although Natural Language Generation techniques do

generate text from the underlying computer based representation, The output text of the existing NLG systems is not of

fairly high quality – this necessitates human intervention when a high quality text is desired

[Sripada et. al, 2003]

In NLG, as opposed to Machine Translation, it is better to automatic evaluation metrics only as a supplement to human evaluations and not as a replacement. [Reiter et. al, 2009]

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References1

Dale, Robert; Reiter, Ehud (2000). Building natural language generation systems. Cambridge, UK: Cambridge University Press

Reiter E, Sripada S, Hunter J, Yu J, Davy I (2005). "Choosing Words in Computer-Generated Weather Forecasts

B. D´ıaz-Agudo, P. Gerv´as, and F. Peinado. A case based reasoning approach to story plot generation. In ECCBR’04, Springer-VerlagLNCS/LNAI, Madrid, Spain, 2004

Reiter E, Anja Belz (2009). An Investigation into the Validity of Some Metrics for Automatically Evaluating Natural Language Generation Systems, Association for Computational Linguistics

http://code.google.com/p/simplenlg/wiki/Section1 M Lapata (2003). Probabilistic Text Structuring: Experiments with

Sentence Ordering. Proceedings of ACL-2003 http://web.science.mq.edu.au/~rdale/teaching/esslli/index.html http://www.wikipedia.org

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References2

E Krahmer, S van Erk, A Verleg (2003). Graph-Based Generation of Referring Expressions. Computational Linguistics

M Poesio, R Stevenson, B di Eugenio, J Hitzeman (2004). Centering: A Parametric Theory and Its Instantiations. Computational Linguistics

R Turner, Y Sripada, E Reiter (2009) Generating Approximate Geographic Descriptions. Proceedings of ENLG-2009

Kathleen R McKeown(1985). Discourse Strategies for Generating Natural-Language Text, Elsevier Science Publishers B.V. (North-Holland)

S. Sripada, E. Reiter, I. Davy (2003), SumTime-Mousam: Congurable marine weather forecast generator, Expert Update 6 (3)

Karin Harbush, Gerard Kempen (2009), Generating clausal coordinate ellipsis multilingually: A uniform approach based on postediting; Proceedings of the 12th European Workshop on Natural Language Generation

Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu(2003) Bleu: a Method for Automatic Evaluation of Machine Translation, IBM Research Division

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Thank You!

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