Natural Language Generation Ling 571 Fei Xia Week 8: 11/17/05

Outline NLG basics –What is NLG? –Example NLG systems –Applications of NLG –Architecture for NLG Three subtasks of NLG –Document planning –Microplanning –Surface realization

NLG basics

What is NLG?

NLU vs. NLG NLU: ambiguity, ill-formed input NLG: choice Text Meaning NLU NLG

NLG system #1: FoG FoG: Forecast Generator Input: weather map Output: textual weather report in English and French Developer: CoGen Tex Status: in operational use since 1992

NLG system #2: SumTime- Mousam FoG: Forecast Generator Input: weather data Output: textual weather report in English Developer: University of Aberdeen Status: Used by one company to generate weather forecasts for offshore oil rigs. Demo

NLG System #3: STOP Input: Questionnaire about smoking attitudes, history, beliefs Output: a personalized smoking-cessation leaflet Developer: University of Aberdeen Demo: non-tailored letter, questionnaire, a tailored letter.Demonon-tailored letterquestionnairea tailored letter Status: undergoing clinical evaluation

Applications of NLG Automated document production: –Weather forecasts, letters, …. Dialogue systems: e.g., ELIZA Text summarization Machine translation Question Answering ….

ElIZA system H: My head hurts E: Why do you say your head hurts? H: My mother hates me. E: Who else in your family hates you? H: *******. E: Tell me more about it. H: What should I do? E: That’s a tough decision. H: ****? E: Does that question interest you?

ELIZA system (cont) The famous 1966 computer system by Joseph Weizenbaum, which acted as a therapist Named after “Eliza Doolittle” in in Shaw's Pygmalion (“My fair lady”). Use pattern matching rules Very successful: prompt him to write a book to explain the limits of computers

Challenges of NLG Decide what to say and how to say it. The output has to be well-formed text. –Orthography, morphology, syntax –Reference, word choice, pragmatics Example: weather report

Component tasks in NLG Content determination: what content to express Document structuring: how to structure the info to make a coherent text. Aggregation: combine units in a document plan tree. Lexicalization: what words to use? Referring expression generation: NP or pronoun? Structure realization: add markups Linguistic realization: tense, aspect, voice, …

Tasks and modules in NLG Document Planning Content determination Document structuring MicroplanningAggregation Lexicalization Referring expression generation Surface realisation Linguistic realization Structure Realization

A pipelined architecture Document Planning Microplanning Surface realization Document plan Text Specification Text Communicative goal Knowledge base Grammar ….

Another architecture Communicative Goal Knowledge base Discourse Planner Surface Realizer NL output Discourse specification

Outline NLG basics –What is NLG? –Example NLG systems –Applications of NLG –Architecture for NLG Three subtasks of NLG –Document planning –Microplanning –Surface realization

Document planning Content determination: –What is important? –a domain-dependent expert-system-like task. –Ex: Weather summary: This Nov was very dry. The temperature was lower. Document structuring: –Use RST or other discourse theory

Document plan tree Msg1 Nucleus Satellite (contrast) Msg2 Nucleus Satellite (Elaboration) Nucleus (sequence) Msg3 …

Microplanning Aggregation Lexicalization Referring expression generation

Aggregation Combinations can be on the basis of –Information content –Possible forms of realization Some possibilities: –Conjunction –Ellipsis –Embedding –…–…

Aggregation via conjunction Without aggregation: –Light rain fell on the 6 th. –Light rain fell on the 8 th. With Aggregation: –Light rain fell on the 6 th and light rain fell on the 8 th. (conjunction) –Light rain fell on the 6 th and the 8 th.

Aggregation via embedding Without aggregation: –November had a rainfall of 20mm. –November normally is the wettest month. With aggregation: –November, which normally is the wettest month, had a rainfall of 20mm this year. –Although November is the wettest month, this November had a rainfall of only 20mm.

Aggregation strategies Conform to genre conventions and rules, and take account of pragmatic goals: –Ex: making sentences shorter for poor readers Observe structural properties: –Ex: aggregate only messages that are siblings in the document plan tree.

An aggregation rule Msg1 NucleusSatellite (contrast) Msg2 S S ConjS S Msg2 Msg1 although

Lexicalization The process of choosing words to communicate the info in messages When several lexicalizations are possible, consider: –User knowledge and preference –Consistency with previous usage: sometimes, it is better to vary lexemes –Pragmatics

Examples Light rain A small amount of rain It is encouraging that you have many reasons to stop. It’s good that you have a lot of reasons to stop.

Referring expression generation How do we identify specific objects and entities? Two cases: –Initial introduction of an object –Subsequence references to an already mentioned object.

Initial reference A few options: Use a full name: John Smith Relate to an entity that is already salient –One of Dr. Klein’s patients –The person who came to the clinic yesterday

Subsequence reference Some possibilities: Pronouns: He is very determined. Definite NPs: This person is very determined. Proper names: John is very talented.

Choosing referring expressions Some suggestions from the literature: –Use a pronoun if it refers to an entity mentioned in the previous clause, and there is no other entity in the previous clause that this pronoun could refer to. –Otherwise, use a name (a short one if possible) –Otherwise, use a definite NP.

Choosing referring expressions (cont) Considering genre conventions and the context Ex: –Nov 2005 –November –This month

Surface realization Goal: to convert text specification into actual text. Structural realization: e.g., add html markup Linguistics realization: –Insert function words –Choose correct inflection –Order words within a sentence –Add punctuation

Linguistics realization Systemic Grammar Functional unification grammar Ex: –Input: (:action rain :tense past :time November :degree little) –Output: it rained little in November

Summary NLG basics –What is NLG? –Example NLG systems –Applications of NLG –Architecture for NLG Three subtasks of NLG –Document planning –Microplanning –Surface realization

Beyond text generation Flat text Structured text: itemized lists, section, chapter, …. Text and graphics: e.g., picture with caption Speech ….

Resources SIGGEN (ACL special interest group for Generation): Book: “Building NLG systems” by Reiter et. al., Cambridge University Press, List of NLG systems:List of NLG systems Companies: –CoGenTex: –ERLI: