Book Review

Building Natural Language Generation Systems.Ehud Reiter and Robert Dale. 2000, Cambridge/New York: Cambridge UniversityPress, -xxi+248 pp.

Reviewer: Roy Wilson, University of Pittsburgh. PA

Introduction

Those interested in exploring the synergies and limitations of natural language proces-sing (NLP) and user modeling will ¢nd this book an indispensable guide.Written overa three-year period, and completed in mid-1998, this book highlights themes and areasof natural language generation (NLG) research that have since been taken up in thedesign and construction of newer systems such as those described inThe Proceedingsof the Second International Natural Language Generation Conference (INLG-02, pub-lished in 2002 by the Association for Computational Linguistics). Students, academics,and developers seeking a single-source foundation for understanding already ¢eldedNLG systems as well as research and development systems will ¢nd it in this book.

The authors regard NLP as a sub¢eld of both computer science and cognitive sci-ence, with NLG and natural language understanding (NLU) as sub¢elds of NLP.The primary problem for NLU is to determine the best interpretation of an inputtext, whereas the primary problem for NLG is to choose the best text to produce.NLU and NLG are not simply ‘reversible’ except at a very abstract level.

The need to choose in NLG creates the possibility of tailoring text, graphics, andspeech to individuals or classes of individuals. For example, as described in the Intro-duction to the book, the PEBA system could (circa1996) interactively produce a hyper-text description of an object that varied by the user’s expertise and prior usage ofthe system. Although the focus of this book is not user modeling, Chapters 5 and7 present material that invites application in a user modeling context.

The authors state they intend to describe NLG from the perspective of what isinvolved in building a ‘complete’ system. Theoretical issues and models are discussedonly to the extent that they directly a¡ect construction. My orientation to NLG theoryand practice is similar, re£ecting both my experience developing an NLG componentof a dialogue-based intelligent tutoring system and my reading in the literature ofthe Meaning-Text Theory that grounds the RealPro surface realizer. I now summarizeeach full chapter in turn.

Summary

In Chapter 1 (Introduction), the authors present a particular NLG architecture. Thearchitecture involves three software modules: text (or document) planning,

User Modeling and User-Adapted Interaction 13: 397^401, 2003. 397# 2003 Kluwer Academic Publishers. Printed in the Netherlands.

sentence (or micro) planning, and text (or surface) realization. The modules are orga-nized as a pipeline: The output of the text planner is input to the sentence planner,the output of the sentence planner is input to the text realizer, and the output ofthe text realizer is ordinary text, hypertext, graphics or speech. The pipeline archi-tecture is considered by many to be the consensus architecture for NLG since manyexisting applied NLG systems are based on it. Before examinining the pipeline archi-tecture more closely, the authors consider the process of building an NLG system.

In Chapter 2 (Natural Language Generation in Practice), the authors o¡er pointersfor building NLG systems that will be regularly used outside the laboratory.

First, assess the costs and bene¢ts of NLG use. For example, since users of anintelligent tutoring system may expect a response within a few seconds, NLG systemdesigners that wish to satisfy this performance constraint will probably have to choosebetween a higher development cost or a simpler approach.

Second, assemble an initial corpus of human-authored texts, the inputs that lead totheir generation, then classify each textual unit of analysis into one of the followingfour categories: unchanging; directly available data; data computable from othersources; and data not derivable from the inputs.

Third, create a target text corpus by appropriately modifying the initial corpus.Some computable elements of the initial text corpus may not justify the cost of theircomputation and should be dropped. The readability of the initial corpus shouldbe improved and spelling errors corrected. If the initial corpus contains texts createdby di¡erent experts, any con£icts between them should be resolved.

Fourth, evaluate the system. Avariety of approaches are available, either black box orglass box.

Fifth, consider factors that might a¡ect the acceptance of NLG technology. Havingsketched an architecture and discussed development issues, the authors turn to £eshingout the pipeline software architecture.

Chapter 3 (The Architecture of a Natural Language Generation System) speci¢esan assignment of particular types of processing tasks to each NLG module, the datastructures they process, and interaction e¡ects generated by the modules. After char-acterizing the system inputs and outputs, the authors provide an overview of eachmodule. After considering each module, task, and intermediate data structure moreclosely, architectural alternatives are presented that range from variants of the pipelinearchitecture to revision-based designs that allow feedback between modules.

Conceiving of language generation as goal-driven communication, the authorsabstractly characterize each input to an NLG system as a tuple hc, k, u, di wherec is the communicative goal to be achieved, k is a source of domain knowledge, uis a model of a user or a class of users, and d is a history of the dialogue betweenthe user and the system. For a particular application, one or more of the componentsof the input tuple may be null. The output of the NLG system, generically referredto as TEXT, may be read from paper or o¡ a screen, or listened to.

Each module is characterized in terms of the tasks it performs, as shown inTable I below. Document structuring refers to the task of grouping (perhaps by section

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or paragraph) blocks of content within a document and deciding how the blocks arerelated, rhetorically or otherwise. Lexicalization involves deciding what words and syn-tactic constructions to use to express the content that has already been determinedby the text planning module.The task of referring expression generation is to decide whatexpression to use to refer to a domain entity. Aggregation is the task of mapping theoutputs of text planning to linguistic structures by deciding, for example, whether anoutput should be realized as a sentence or a paragraph. Converting the output ofthe sentence planner into actual text is the task of linguistic realization. Structure reali-zation is the task of making an abstract representation of sections and paragraphs (ifthey exist) interpretable by a document presentation component (such as a browser) thatis external to the NLG system. Chapters 4 to 6, inclusive, examine in greater detailthe pipeline architecture and the issues overviewed in the ¢rst three chapters.

Chapter 4 (Document Planning) deepens the discussion of text planning begun inthe previous chapter. The input to the text planner is the tuple hc, k, u, di describedearlier. The authors de¢ne a message as information about particular relationsand properties involving domain elements. For the content-determination task, theypresent corpus-based methodologies for: modeling an NLG application domainand de¢ning messages; constructing rules that determine which messages ought toappear in a particular text. Document-structuring involves constructing the text planby grouping messages, ordering them, specifying which groups correspond to para-graphs or sections, and indicating whatever discourse relations that hold betweenmessages or message groups. The document-structuring task can be implementedusing a top-down schema-based approach or a bottom-up approach. The authorsdescribe several architectural possibilities for a text planner that result from eitherinterleaving or pipelining the content-determination and document-structuring tasks.The output of the text planner is a tree (a text plan) with leaves that specify messagesand internal nodes that specify discourse relations between messages.

Chapter 5 (Microplanning) describes in greater detail the tasks that the sentenceplanning module must complete in order to transform a text plan into a text speci-¢cation suitable as input to the realization module.The sentence planner must decide:what words and syntactic constructs to use to communicate a message (lexicalization);how many messages each sentence should carry (aggregation); how to refer to entitiesin the domain when communicating with the user (referring expression generation).The authors describe a number of types of lexical choice and describe a set of aggre-gation mechanisms.

Table I. Modules and tasks.

Module Content task Structure task

Text planning Content determination Document structuringSentence planning Lexicalization; Referring

expression generationAggregation

Realization Linguistic realization Structure realization

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Although having aggregation follow lexicalization and precede referring expressiongeneration is not necessary, a pipeline (versus interleaved) architecture is easier tobuild, and may provide real-time performance. Although the authors do not stateit, NLG systems can draw on user models to vary how lexicalization and aggregationare carried out.

In Chapter 6 (Surface Realization), the authors focus on linguistic realization: theprocess of mapping text speci¢cations to surface-form sentences. Because of thecapabilities provided by existing systems, it is often unnecessary to build a linguisticrealization component. Moreover, sometimes even a canned text (or variant) approachto NLG is su⁄cient.

The authors brie£y describe three existing linguistic realization systems: KPML,SURGE, and RealPro.The ¢rst two systems are based on Systemic Functional Gram-mar, while RealPro is based onMeaning-Text Theory.The authors give a brief overviewof each of these two grammatical theories.

Ignoring di¡erences in the theoretical commitments entailed by the use of each textrealizer, the most important issue in choosing among the three systems mentionedabove is, according to the authors, the level of abstraction in the text speci¢cationthat serves as realizer input. The Deep Syntactic Structures required by RealProare in fact less abstract than the meaning speci¢cations required by KPML, whichin turn are less abstract than the lexicalized case frames needed by SURGE.The moreabstract the realizer input, the less work done by text planner and the more doneby the realizer. If it is desirable or necessary to have non-linguists create (possiblygeneralized) templates that the text planner will use to produce text speci¢cations,RealPro may be preferrable. If the text planning module is to create text speci¢cationslargely from scratch, the use of KPML or SURGE may be preferrable as they areable to take over much more of the work of the text planner. Although all three lin-guistic realization systems have been widely used, only RealPro (the simplest) has beencommercially ¢elded.

Whereas the previous chapter addresses linguistic realization, Chapter 7 (BeyondText Generation) focuses on structure realization and generation of complex docu-ments as opposed to simple texts. The use of an (external) document presentationsystem imposes on an NLG system the need to create typographic distinctions inits output, which serves as input to the document presentation system. Given the abilityto generate typographic annotations, it is possible to consider the generation of textand graphics, hypertext, and speech. The authors discuss implementation issuesfor each type of generation as it a¡ects the text planning, sentence planning, andrealization modules.

NLG systems o¡er the possibility of generating speech that is responsive to thecharacteristics of individual users and classes of users. The most complicated aspectof generating synthesized speech is the assignment of prosody: that is, the determi-nation of the pitch, speed, and volume of spoken syllables, words, phrases, and sen-tences. According to the authors, communicative goals must be known in order toproperly assign prosody to the intonation units associated with an utterance.

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Since an NLG system already has access to these goals, the possibility exists, as sug-gested by Hovy and Fleischman at INLG-02, of concept-to-speech based NLG sys-tems that provide emotional variation in their responses to users.

Commentary

This book is extremely well-organized and well-written. Although oriented to the pro-duction of structured documents, the expository framework is broad enough to beuseful in designing an NLG component of a dialogue-based intelligent tutoring sys-tem. Discussion throughout of the hypothetical WeatherReporter system allowsthe authors to construct examples that provide elaboration and contrast.

The organizing principle for the book seems to be drawn from the practice of soft-ware engineering. The assumption of a pipelined architecture eases the expositionand makes it easy to appreciate the strengths and limitations of that architectureas well as alternatives. Because the issues are clearly articulated and well-referenced,readers can, as suggested earlier, move to accounts that are more detailed, more recent,or both.

RoyWilson is an AI programmer and NLG researcher with the Learning ResearchDevelopment Center at the University of Pittsburgh, Pennsylvania.

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