1. Jan 2004CSA3050: NLG21 CSA3050: Natural Language Generation 2 Surface Realisation Systemic Grammar Functional Unification Grammar see J&M Chapter 20.3

2. Jan 2004CSA3050: NLG22 Surface Realisation within NLG

3. Jan 2004CSA3050: NLG23 Revealing Quotations Language understanding is somewhat like counting from one to infinity; language generation is like counting from infinity back to one. [Y. Wilks, 1988] Generation from what?! [C. Longuet- Higgins] Moral: there is no consensus about the level of representation we start from. This applies in particular to surface realisation.

4. Jan 2004CSA3050: NLG24 Surface Realisation There is, however, some agreement that output should be specified in terms of function rather than syntactic form Consider the case of voice (syntactic form) The system will save the document vs. The document will be saved by the system Output of a discourse planner is not in terms of voice, active v. passive. More likely in terms of notions like topic, focus etc. whose surface realisation is left to a subsequent processing stage.

5. Jan 2004CSA3050: NLG25 Two Approaches to Surface Realisation Systemic Grammar (Halliday 1985) –Systemic Functional Linguistics –Language as a resource for expressing meaning in context. –Sentences are regarded collections of functions that are mapped to grammatical forms. Functional Unification Grammar (Kay 1979) –Grammar is a "feature structure" (recursive attribute/value pairs). –Input specification is another feature structure –Realisation viewed as further specification of input to yield output sentence via fundamental operation of functional unification.

6. Jan 2004CSA3050: NLG26 Systemic Grammar: Multi-Layered Sentence Analysis the systemwillsavethe document Moodsubjectfinitepredicatorobject Transitivityactorprocessgoal Themethemerheme Mood layer defines interaction between reader and writer: telling v. asking v. ordering Transitivity layer expresses propositional content Theme layer deals with topic (highlighting function)

7. Jan 2004CSA3050: NLG27 Systemic Grammar Grammar is represented as a graph called a system network. This comprises and systems (curly braces) –conjunctive features in boldface or systems (straight vertical lines) –disjunctive features in normal face realisation statements (in italic). –specify how disjunctive features are realised

8. Jan 2004CSA3050: NLG28

9. Jan 2004CSA3050: NLG29 Realisation Statements +X: insert the function X +predicator X=Y: conflate the functions X and Y goal = subject X>Y: order X somewhere before Y subject > predicator X/A: function X has grammatical feature Y subject/noun phrase X!L assign function X to lexical item L passive!be

10. Jan 2004CSA3050: NLG210 Generation Procedure 1.Traverse network from left to right, choosing the appropriate features and collecting the appropriate realisation statements. 2.Build an intermediate expression that obeys the constraints set by the realisation statements. 3.Recurse back through the grammar at a lower level for functions not fully specified.

11. Jan 2004CSA3050: NLG211 Example Input ( :process save-1 :actor system-1 :goal document-1 :speechact assertion :tense future ) N.B. This specification resembles that used by PENMAN system (Mann 1983) based on KL- ONE knowledge base.

12. Jan 2004CSA3050: NLG212 Generation 1 Start with clause feature –insert predicator and classify as verb Proceed to mood subsystem after interrogating input specification (assertion is specified) choose indicative and declarative features. Insert subject and finite functions with ordering subject > finite > predicator

13. Jan 2004CSA3050: NLG213 Output of Mood System Moodsubjectfinitepredicator

14. Jan 2004CSA3050: NLG214 Generation 2 Proceed to Transitivity subsystem. Assuming save-1 is a material process (from KB) –Insert goal and process functions –Conflate process with (finite, predicator) pair –Proceed to Voice subsystem –Choose Active feature and obey realisation statements.

15. Jan 2004CSA3050: NLG215 Output of Transitivity System Moodsubjectfinitepredicatorobject Transitivityactorprocessgoal

16. Jan 2004CSA3050: NLG216 Generation 3 Proceed to Theme subsystem Since there is no thematic specification in the input, choose Unmarked Theme –Insert theme and rheme functions –Conflate theme with subject and rheme with predicator,object

17. Jan 2004CSA3050: NLG217 Output of Mood Subsystem Moodsubjectfinitepredicatorobject Transitivityactorprocessgoal Themethemerheme

18. Jan 2004CSA3050: NLG218 Generation 4 Generation now recursively enters the grammar at lower levels to fully specify –the phrases (noun phrase network, auxiliary network – not shown here) –lexical items (usually through entries in the knowledge base) –and possibly morphological choices

19. Jan 2004CSA3050: NLG219 Result of Generation the systemwillsavethe document Moodsubjectfinitepredicatorobject Transitivityactorprocessgoal Themethemerheme

20. Jan 2004CSA3050: NLG220 Functional Unification Grammar Build the generation grammar as a feature structure (FS) Build input specification as an FS Unify the two together (functional unification operation). Linearize result.

21. Jan 2004CSA3050: NLG221 Feature Structures Feature structures are essentially sets of attribute value pairs attribute1value1 attribute2value2 : attribute3value3

22. Jan 2004CSA3050: NLG222 Feature Structure 2 These can be used to express facts of different kinds namemike height156 : eyesgreen numbersing gendermasc : casenom human factsagreement facts

23. Jan 2004CSA3050: NLG223 Feature Structures 3 Furthermore, values can themselves be feature structures numbersing gendermasc casenom catNP agreement

24. Jan 2004CSA3050: NLG224 Functional Unification The information in feature structures can be combined together by means of the functional unification (  ) operation. namemike height156 eyesgreen namemike height156 eyesgreen  =

25. 25 Grammar as a Feature Structure

26. Jan 2004CSA3050: NLG226 Input Specification – as a Feature Structure catS actorheadlexsystem processheadlexsave tensefuture goalheadlexdocument

27. 27 Partial Result catS actorcatNP headlexsystem processcatVP number{actor number} headlexsave tensefuture goalcatNP headlexdocument pattern(actor process goal)

28. 28 Full Result