Natural Language Generation Martin Hassel KTH NADA Royal Institute of Technology Stockholm

2Martin Hassel What Is Natural Language Generation? A process of constructing a natural language output from non linguistic inputs that maps meaning to text.

3Martin Hassel Related Simple Text Generation Canned text Ouputs predefined text Template filling Outputs predefined text with predefined variable words/phrases

4Martin Hassel Areas of Use NLG techniques can be used to: generate textual weather forecasts from representations of graphical weather maps summarize statistical data extracted from a database or spreadsheet explain medical info in a patient-friendly way describe a chain of reasoning carried out by an expert system paraphrase information in a diagram for inexperienced users

5Martin Hassel Goals of a NLG System To supply text that is: correct and relevant information non redundant suiting the needs of the user in an understandable form in a correct form

6Martin Hassel Choices for NLG Content selection Lexical selection Sentence structure Aggregation Referring expressions Orthographic realisation Discourse structure

7Martin Hassel Example Architecture Discourse Specification Knowledge Base Communicative Goal Natural Language Output Discourse Planner Surface Realizer

8Martin Hassel Discourse Planner 1. Text shemata Use consistent patterns of discourse structure 2. Rhetorical Relations Uses the Rhetorical Structure Theory Used for varied generation tasks

9Martin Hassel Discourse Planner – Text Schemata Augmented Transition Network (ATN) Uses a set of internal states and transitions Produces texts with a rigid structure i.e. recipes, directions, technical instructions

10Martin Hassel Discourse Planner – Augmented Transition Network

11Martin Hassel Discourse Planner – Augmented Transition Network Example output: Save the document: First, choose the save option from the file menu. This causes the system to display the Save-As dialog box. Next, choose the destination folder and type the filename. Finally, press the save button. This causes the system to save the document.

12Martin Hassel Discourse Planner – Rhetorical Relations Rhetorical Structure Theory (Mann & Thompson 1988) Nucleus Multi-nuclear Satellite

13Martin Hassel Discourse Planner – Rhetorical Relations 23 rhetorical relations, among these: Elaboration Contrast Condition Purpose Sequence Result

14Martin Hassel Discourse Planner – Rhetorical Relations Rethorical annotation:

15Martin Hassel Discourse Planner – Rhetorical Relations The resulting RST tree corpus: (SATELLITE(SPAN|4||19|)(REL2PAR ELABORATION- ADDITIONAL) (SATELLITE(SPAN|4||7|)(REL2PAR CIRCUMSTANCE) (NUCLEUS(LEAF|4|)(REL2PAR CONTRAST) (TEXT _!THE PACKAGE WAS TERMED EXCESSIVE BY THE BUSH |ADMINISTRATION,_!|)) (NUCLEUS(SPAN|5||7|)(REL2PAR CONTRAST) (NUCLEUS(LEAF|5|)(REL2PAR SPAN) (TEXT _!BUT IT ALSO PROVOKED A STRUGGLE WITH INFLUENTIAL CALIFORNIA LAWMAKERS_!))

16Martin Hassel Surface Realisation 1. Systemic Grammar Represents sentences as collections of functions Using functional categorization 2. Functional Unification Grammar Builds generation grammar as a feature structure Using functional categorization

17Martin Hassel Surface Realisation – Systemic Grammar Emphasises the functional organisation of language Surface forms are viewed as the consequences of selecting a set of abstract functional features Choices correspond to minimal grammatical alternatives The interpolation of an intermediate abstract representation allows the specification of the text to accumulate gradually

18Martin Hassel Surface Realisation – Systemic Grammar Uses multiple layers Mood, Transivity, Theme Meta-functions Interpersonal meta-function (mood) Ideational meta-function (transivity) Textual meta-function (theme) Grammar represented as a system network Directed, acyclic and/or graph

19Martin Hassel Surface Realisation – Systemic Grammar Present-Participle Past-Participle Infinitive Polar Wh- Imperative Indicative Declarative Interrogative Major Minor Mood … Bound Relative

20Martin Hassel Surface Realisation – Systemic Grammar Clause Choices Major indicative declarative: The cat is on the mat. Major indicative declarative relative: [He didn’t see the cat] that chased the rat. Major indicative declarative bound: [It only hurts] when I laugh. Major indicative interrogative polar: Has anybody seen my seagull? Major imperative: Don’t be ridiculous. Minor present-participle: [You’ll enjoy] having more free time.

21Martin Hassel Surface Realisation – Functional Unification Grammar Basic idea: Input specification in the form of a FUNCTIONAL DESCRIPTION, a recursive matrix The grammar is a large functional description with alternations representing choice points Realisation is achieved by unifying the input FD with the grammar FD

22Martin Hassel Surface Realisation – Functional Unification Grammar ((cat clause) (process ((type composite) (relation possessive) (lex ‘hand’))) (participants ((agent ((cat pers_pro) (gender feminine))) ((affected  ((cat np) (lex ‘editor’))) ((possessor  )) ((possessed ((cat np) (lex ‘draft’))))) She hands the draft to the editor.

23Martin Hassel Microplanners 1 Lexical selection Syntactic realization Morphological realization Orthographic realization

24Martin Hassel Microplanners 2 Referring Expression Generation Referring expression generation is concerned with how we describe domain entities in such a way that the hearer will know what we are talking about Major issue is avoiding ambiguity Fluency pulls in the opposite direction

25Martin Hassel Kinds of Referring Expressions Proper names Aberdeen, Scotland Aberdeen Definite Descriptions the train that leaves at 10am the next train Pronouns she it

26Martin Hassel Microplanners 3 Aggregation Some possibilities: Simple conjunction Ellipsis Set introduction

27Martin Hassel Aggregation Without aggregation: The next train is the Caledonian Express. It leaves at 10AM. With Simple Conjunction : The next train is the Caledonian Express, and it leaves at 10AM. Without aggregation: It leaves at 10AM. It arrives at 12.30PM. With ellipsis: It leaves at 10AM, and Ø arrives at 12.30PM.

28Martin Hassel Aggregation Without aggregation: It has a snack bar. It has a restaurant car. With set introduction : It has {a snack bar, a restaurant car}. It has a snack bar and a restaurant car. Caution! Need to avoid changing the meaning: John bought a TV and Bill bought a TV. John and Bill bought a TV.

29Martin Hassel Further Reading Siggen Allen 1995: Natural Language Understanding cl/Allen95/al1995co.htm