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Automated Writing Assistance:
             Grammar Checking and Beyond
             Topic 5: Beyond the Sentence
                         Robert Dale
              Centre for Language Technology
                    Macquarie University


SSLST 2011                                     1
Outline

       • Frontiers: Where We Might Want to Go
       • The View from Natural Language Generation
       • Closing Remarks




SSLST 2011                                           2
Frontiers

       • Writing assistance …
          – Beyond the sentence
          – Beyond syntax
          – Beyond revision




SSLST 2011                        3
Flower and Hayes’ Cognitive Process Model




                            Flower and Hayes 1981

SSLST 2011                                          4
The Nature of the Revision Process




                             Faigley and Witte 1981
SSLST 2011                                            5
Outline

       • Frontiers: Where We Might Want to Go
       • The View from Natural Language Generation
       • Closing Remarks




SSLST 2011                                           6
What is NLG?

       • Goal:
           – computer software which produces understandable texts in
             English or other human languages
       • Input:
           – some underlying non-linguistic representation of
             information
       • Output:
           – documents, reports, explanations, help messages, and other
             kinds of texts

SSLST 2011                                                 7
NLP = NLU + NLG

             Natural Language               Natural Language
              Understanding                    Generation

                                ‘Meaning’




                       Text                 Text

        8
SSLST 2011
Inputs and Outputs

       The inputs to NLG:
       • A knowledge source
       • A communicative goal
       • A user model
       • A discourse model
       The output of NLG:
       • A text, possibly embodied as part of a document or within a
         speech stream

        9
SSLST 2011
Component Tasks in NLG

      1      Content determination
      2      Discourse planning
      3      Sentence aggregation
      4      Lexicalisation
      5      Referring expression generation
      6      Syntactic and morphological realization
      7      Orthographic realization


        10
SSLST 2011
1            Content Determination

      • The process of deciding what to say
      • Can be viewed as the construction of a set of MESSAGES from
        the underlying data source
      • Messages are aggregations of data that are appropriate for
        linguistic expression: each may correspond to the meaning of
        a word or a phrase
      • Messages are based on domain entities, concepts, and
        relations


        11
SSLST 2011
2            Discourse Planning

      • A text is not just a random collection of sentences
      • Texts have an underlying structure in which the parts are
        related together
      • Two related issues:
          – conceptual grouping
          – rhetorical relationships


                                                       

        12
SSLST 2011
3            Sentence Aggregation

      • A one-to-one mapping from messages to sentences results in
        disfluent text
      • Messages need to be combined to produce larger and more
        complex sentences
      • The result is a sentence specification or SENTENCE PLAN




        13
SSLST 2011
4            Lexicalisation

      • So far we have determined text content and the structuring of
        the information into paragraphs and sentences, but the raw
        material is still assumed to be in the form of a conceptual
        representation
      • Lexicalisation determines the particular words to be used to
        express domain concepts and relations




        14
SSLST 2011
5            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
      • Do we use a proper name? A definite or indefinite description?
        A pronoun?




        15
SSLST 2011
6            Syntactic and Morphological Realization

      • Every natural language has grammatical rules that govern how
        words and sentences are constructed
         – Morphology: rules of word formation
         – Syntax: rules of sentence formation




        16
SSLST 2011
7 Orthographic Realization

      • Orthographic realization is concerned with matters like casing
        and punctuation
      • This also extends into typographic issues: font size, column
        width …




        17
SSLST 2011
Tasks and Architecture in NLG

      •      Content determination               Document
      •      Discourse planning                  Planning
      •      Sentence aggregation
      •      Lexicalisation                    Micro Planning
      •      Referring expression generation
      •      Syntax + morphology                 Linguistic
      •      Orthographic realization           Realization



        18
SSLST 2011
A Pipelined Architecture
                      Document
                      Planning

                     Document Plan

                    Microplanning


                   Text Specification

                       Surface
                      Realisation

        19
SSLST 2011
Microplanning Help

       • Paraphrase
       • Sentence simplification via summarisation techniques




SSLST 2011                                                      20
Aggregation

      Combinations can be on the basis of
      • information content
      • possible forms of realisation
      Some possibilities:
      • Simple conjunction
      • Ellipsis
      • Embedding
      • Set introduction

        21
SSLST 2011
Some Examples
       Without aggregation:
             – Heavy rain fell on the 27th.
               Heavy rain fell on the 28th.
       With aggregation via simple conjunction:
             – Heavy rain fell on the 27th and heavy rain fell
               on the 28th.
       With aggregation via ellipsis:
             – Heavy rain fell on the 27th and [] on the 28th.
       With aggregation via set introduction:
             – Heavy rain fell on [the 27th and 28th].


        22
SSLST 2011
An Example: Embedding

      Without aggregation:
             – March had a rainfall of 120mm.
               It was the wettest month.


      With aggregation:
             – March, which was the wettest month, had a
               rainfall of 120mm.




        23
SSLST 2011
Rhetorical Structure Theory

       • Basic idea:
          – The elements of a text are connected together by rhetorical
            relations
          – A text is coherent by virtue of the presence of these
            relations---if the text cannot be analysed in these terms
            then it is not coherent.




SSLST 2011                                                                24
Text Structure

       You should come to the Northern Beaches Ballet performance on
       Saturday. I’m in three pieces. The show is really good. It got a
       rave review in the Manly Daily. You can get the tickets from the
       shop next door.




SSLST 2011                                                                25
Beyond Pairs of Sentences

       S1: You should come to the Northern Beaches Ballet
           performance on Saturday.
       S2: I’m in three pieces.
       S3: The show is really good.
       S4: It got a rave review in the Manly Daily.
       S5: You can get the tickets from the shop next door.




SSLST 2011                                                    26
The Ballet Text

                                                           ENABLEMENT
                                   MOTIVATION




                              MOTIVATION                   EVIDENCE




             You should ...    I’m in ...   The show ...   It got a ...   You can get ...


SSLST 2011                                                                                  27
An RST Relation Definition

      Relation name: Motivation
      Constraints on N:
           Presents an action (unrealised) in which the hearer is the
           actor
      Constraints on S:
           Comprehending S increases the hearer’s desire to perform
           the action presented in N
      The effect:
           The hearer’s desire to perform the action presented in N is
           increased
        28
SSLST 2011
Outline

       • Frontiers: Where We Might Want to Go
       • The View from Natural Language Generation
       • Closing Remarks




SSLST 2011                                           29
Conclusions

       • Current technology only scratches the surface in terms of the
         kinds of support we would like to give to authors
       • Almost any aspect of NLP technology can be pressed into
         service to support authors
       • NLG techniques provide a rich source of ideas for how to build
         symbiotic systems that take advantage of the knowledge and
         capabilities of both human and machine




SSLST 2011                                                                30
Who Today’s Main Players Are

       •     Google
       •     Microsoft
       •     Educational Testing Service
       •     Activities around the University of Cambridge




SSLST 2011                                                   31
Finding Out More

       • ACL Workshops on Innovative Use of NLP for Building
         Educational Applications: 2011 was the sixth in the series
       • Relevant material often found in journals outside the normal
         ‘ACL space’:
           CALICO Journal
           College Composition and Communication
           Computers and Composition
           Computer Assisted Language Learning,
           Journal of Second Language Writing


SSLST 2011                                                              32

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Tarragona Summer School/Automated Writing Assistance Topic 5

  • 1. Automated Writing Assistance: Grammar Checking and Beyond Topic 5: Beyond the Sentence Robert Dale Centre for Language Technology Macquarie University SSLST 2011 1
  • 2. Outline • Frontiers: Where We Might Want to Go • The View from Natural Language Generation • Closing Remarks SSLST 2011 2
  • 3. Frontiers • Writing assistance … – Beyond the sentence – Beyond syntax – Beyond revision SSLST 2011 3
  • 4. Flower and Hayes’ Cognitive Process Model Flower and Hayes 1981 SSLST 2011 4
  • 5. The Nature of the Revision Process Faigley and Witte 1981 SSLST 2011 5
  • 6. Outline • Frontiers: Where We Might Want to Go • The View from Natural Language Generation • Closing Remarks SSLST 2011 6
  • 7. What is NLG? • Goal: – computer software which produces understandable texts in English or other human languages • Input: – some underlying non-linguistic representation of information • Output: – documents, reports, explanations, help messages, and other kinds of texts SSLST 2011 7
  • 8. NLP = NLU + NLG Natural Language Natural Language Understanding Generation ‘Meaning’ Text Text 8 SSLST 2011
  • 9. Inputs and Outputs The inputs to NLG: • A knowledge source • A communicative goal • A user model • A discourse model The output of NLG: • A text, possibly embodied as part of a document or within a speech stream 9 SSLST 2011
  • 10. Component Tasks in NLG 1 Content determination 2 Discourse planning 3 Sentence aggregation 4 Lexicalisation 5 Referring expression generation 6 Syntactic and morphological realization 7 Orthographic realization 10 SSLST 2011
  • 11. 1 Content Determination • The process of deciding what to say • Can be viewed as the construction of a set of MESSAGES from the underlying data source • Messages are aggregations of data that are appropriate for linguistic expression: each may correspond to the meaning of a word or a phrase • Messages are based on domain entities, concepts, and relations 11 SSLST 2011
  • 12. 2 Discourse Planning • A text is not just a random collection of sentences • Texts have an underlying structure in which the parts are related together • Two related issues: – conceptual grouping – rhetorical relationships   12 SSLST 2011
  • 13. 3 Sentence Aggregation • A one-to-one mapping from messages to sentences results in disfluent text • Messages need to be combined to produce larger and more complex sentences • The result is a sentence specification or SENTENCE PLAN 13 SSLST 2011
  • 14. 4 Lexicalisation • So far we have determined text content and the structuring of the information into paragraphs and sentences, but the raw material is still assumed to be in the form of a conceptual representation • Lexicalisation determines the particular words to be used to express domain concepts and relations 14 SSLST 2011
  • 15. 5 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 • Do we use a proper name? A definite or indefinite description? A pronoun? 15 SSLST 2011
  • 16. 6 Syntactic and Morphological Realization • Every natural language has grammatical rules that govern how words and sentences are constructed – Morphology: rules of word formation – Syntax: rules of sentence formation 16 SSLST 2011
  • 17. 7 Orthographic Realization • Orthographic realization is concerned with matters like casing and punctuation • This also extends into typographic issues: font size, column width … 17 SSLST 2011
  • 18. Tasks and Architecture in NLG • Content determination Document • Discourse planning Planning • Sentence aggregation • Lexicalisation Micro Planning • Referring expression generation • Syntax + morphology Linguistic • Orthographic realization Realization 18 SSLST 2011
  • 19. A Pipelined Architecture Document Planning Document Plan Microplanning Text Specification Surface Realisation 19 SSLST 2011
  • 20. Microplanning Help • Paraphrase • Sentence simplification via summarisation techniques SSLST 2011 20
  • 21. Aggregation Combinations can be on the basis of • information content • possible forms of realisation Some possibilities: • Simple conjunction • Ellipsis • Embedding • Set introduction 21 SSLST 2011
  • 22. Some Examples Without aggregation: – Heavy rain fell on the 27th. Heavy rain fell on the 28th. With aggregation via simple conjunction: – Heavy rain fell on the 27th and heavy rain fell on the 28th. With aggregation via ellipsis: – Heavy rain fell on the 27th and [] on the 28th. With aggregation via set introduction: – Heavy rain fell on [the 27th and 28th]. 22 SSLST 2011
  • 23. An Example: Embedding Without aggregation: – March had a rainfall of 120mm. It was the wettest month. With aggregation: – March, which was the wettest month, had a rainfall of 120mm. 23 SSLST 2011
  • 24. Rhetorical Structure Theory • Basic idea: – The elements of a text are connected together by rhetorical relations – A text is coherent by virtue of the presence of these relations---if the text cannot be analysed in these terms then it is not coherent. SSLST 2011 24
  • 25. Text Structure You should come to the Northern Beaches Ballet performance on Saturday. I’m in three pieces. The show is really good. It got a rave review in the Manly Daily. You can get the tickets from the shop next door. SSLST 2011 25
  • 26. Beyond Pairs of Sentences S1: You should come to the Northern Beaches Ballet performance on Saturday. S2: I’m in three pieces. S3: The show is really good. S4: It got a rave review in the Manly Daily. S5: You can get the tickets from the shop next door. SSLST 2011 26
  • 27. The Ballet Text ENABLEMENT MOTIVATION MOTIVATION EVIDENCE You should ... I’m in ... The show ... It got a ... You can get ... SSLST 2011 27
  • 28. An RST Relation Definition Relation name: Motivation Constraints on N: Presents an action (unrealised) in which the hearer is the actor Constraints on S: Comprehending S increases the hearer’s desire to perform the action presented in N The effect: The hearer’s desire to perform the action presented in N is increased 28 SSLST 2011
  • 29. Outline • Frontiers: Where We Might Want to Go • The View from Natural Language Generation • Closing Remarks SSLST 2011 29
  • 30. Conclusions • Current technology only scratches the surface in terms of the kinds of support we would like to give to authors • Almost any aspect of NLP technology can be pressed into service to support authors • NLG techniques provide a rich source of ideas for how to build symbiotic systems that take advantage of the knowledge and capabilities of both human and machine SSLST 2011 30
  • 31. Who Today’s Main Players Are • Google • Microsoft • Educational Testing Service • Activities around the University of Cambridge SSLST 2011 31
  • 32. Finding Out More • ACL Workshops on Innovative Use of NLP for Building Educational Applications: 2011 was the sixth in the series • Relevant material often found in journals outside the normal ‘ACL space’: CALICO Journal College Composition and Communication Computers and Composition Computer Assisted Language Learning, Journal of Second Language Writing SSLST 2011 32