## A Translation from Logic to English with Dynamic Semantics

author: Elizabeth Coppock, Cycorp Inc.
published: March 26, 2010,   recorded: March 2010,   views: 786
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# Slides

0:00 Slides A Translation from logic to English with Dynamic Semantics Outline - 1 General Program Referring and non-referring expressions Two separate fields in NLG Generating Referring Expressions - 1 Generating Referring Expressions - 2 Generating Referring Expressions - 3 Generating Referring Expressions - 4 Generating "quantified" referring expressions Tactical generation Quantificational input How to combine strenghts - 1 How to combine strenghts - 2 How to combine strenghts - 3 Dynamic Semantics - 1 Dynamic Semantics - 2 Dynamic Semantics - 3 Karttunen's discourse referents - 1 Karttunen's discourse referents - 2 DRT Example - 1 DRT Example - 2 DRT Example - 3 Discourse Representation Structures -1 Discourse Representation Structures -2 Discourse Representation Structures -3 A DRS is not a good point - 1 A DRS is not a good point - 2 A DRS is not a good point - 3 Present framework - 1 Present framework - 2 Present framework - 3 Outline - 2 Direct translation A simple algorithm The simple algorithm works in simple cases Exception: Donkey sentences Universally quantified variables under negation No any in subject position Relative scope matters Existentials - indefinite Existentially bound variables and negation Existentially bound variables and negation, cont'd Everyone needs anaphora Short term referents - 1 Short term referents - 2 Short term referents - 3 Short term referents - 4 A better example - 1 A better example - 2 A better example - 3 A better example - 4 Different determiners, different lifespans Our solution: Side effects - 1 Our solution: Side effects - 2 Our solution: Side effects - 3 Outline - 3 Discourse context Letting discourse referents be constants - 1 Letting discourse referents be constants - 2 Letting discourse referents be constants - 3 What makes the semantics dynamic Example - 1 Example - 2 Example - 3 Example - 4 Example - 5 Example - 6 Generation templates Discourse referents corresponding to variables - 1 Discourse referents corresponding to variables - 2 Discourse referents corresponding to variables - 3 Discourse referents corresponding to variables - 4 Discourse referents corresponding to variables - 5 Discourse referents corresponding to variables - 6 Lifespan limitations - 1 Lifespan limitations - 2 Lifespan limitations - 3 Lifespan limitations - 4 Lifespan limitations - 5 Lifespan limitations - 6 Lifespan limitations - 7 Lifespan limitations - 8 Lifespan limitations - 9 Lifespan limitations - 10 Lifespan limitations - 11 Lifespan limitations - 12 Lifespan limitations - 13 Suppose we did not remove x from D - 1 Suppose we did not remove x from D - 2 Suppose we did not remove x from D - 3 Suppose we did not remove x from D - 4 Suppose we did not remove x from D - 5 Suppose we did not remove x from D - 6 Suppose we did not remove x from D - 7 Suppose we did not remove x from D - 8 Suppose we did not remove x from D - 9 Suppose we did not remove x from D - 10 Suppose we did not remove x from D - 11 Returning to Heim's cat example - 1 Returning to Heim's cat example - 2 Returning to Heim's cat example - 3 Returning to Heim's cat example - 4 Outline - 4 Operator Context: Definition - 1 Operator Context: Definition - 2 Clausal skeletons Variable typing clauses - 1 Variable typing clauses - 2 Variable typing clauses - 3 Variable typing clauses - 4 Constructing clausal skeletons - 1 Constructing clausal skeletons - 2 Procedure - 1 Procedure - 2 Procedure - 3 Example: Donkey sentence - 1 Example: Donkey sentence - 2 Example: Donkey sentence - 3 Example: Eliminating the antecedent - 1 Example: Eliminating the antecedent - 2 Example: Eliminating the antecedent - 3 Negation stripping - 1 Negation stripping - 2 Negation stripping - 3 Negation stripping - 4 Updating the operator stack Variable realization algorithm Determiner selection algorithm - 1 Determiner selection algorithm - 2 Determiner selection algorithm - 3 Example: nothing - 1 Example: nothing - 2 Example: nothing - 3 Example: nothing - 4 Example: nothing - 5 Example: nothing - 6 Example: nothing - 7 Example: nothing - 8 Example: nothing - 9 Example: anything - 1 Example: anything - 2 Example: anything - 3 Example: anything - 4 Example: anything - 5 Example: anything - 6 Example: anything - 7 Example: anything - 8 Example: anything - 9 Example: anything - 10 Example: not ... everything - 1 Example: not ... everything - 2 Example: not ... everything - 3 Example: not ... everything - 4 Example: not ... everything - 5 Example: not ... everything - 6 Example: not ... everything - 7 Example: not ... everything - 8 Example: not ... everything - 9 Example: not ... everything - 10 Example: Donkey sentence - 4 Example: Donkey sentence - 5 Example: Donkey sentence - 6 Example: Donkey sentence - 7 Example: Donkey sentence - 8 Example: Donkey sentence - 9 Example: Donkey sentence - 10 Example: Donkey sentence - 11 Example: Donkey sentence - 12 Example: Donkey sentence - 13 Example: Donkey sentence - 14 Example: Donkey sentence - 15 Interaction between determiners and lifespans - 1 Interaction between determiners and lifespans - 2 Interaction between determiners and lifespans - 3 Interaction between determiners and lifespans - 4 Interaction between determiners and lifespans - 5 Interaction between determiners and lifespans - 6 Interaction between determiners and lifespans - 7 Interaction between determiners and lifespans - 8 Interaction between determiners and lifespans - 9 Interaction between determiners and lifespans - 10 Interaction between determiners and lifespans - 11 Interaction between determiners and lifespans - 12 Outline - 5 Summary - 1 Summary - 2 Summary - 3 Outlook Thank you

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# Description

We present a procedure for translating standard predicate logic into English. The procedure generates both referring expressions and non-referring expressions, including both referential and bound variable anaphora. Non-referring expressions correspond to short-term discourse referents [1], which present a special set of challenges for a natural language generation system: (i) they have limited ‘lifespans’ and (ii) the determiner with which they are introduced (every, some, any, no) is sensitive to the logical context. Our system addresses these challenges using dynamically updated information states.