Chapter 20: Natural Language Generation Presented by: Anastasia Gorbunova LING538: Computational Linguistics, Fall 2006 Speech and Language Processing (Jurafsky & Martin)
Natural Language Generation (NLG) The process of constructing natural language outputs from non-linguistic inputs - maps from meaning to text Concerns: - choice Natural Language Understanding (NLU) The process of producing non-linguistic outputs from natural language inputs hypothesis management - maps from text to meaning Concerns: - ambiguity - under-specification - ill-formed input Both must represent a range of lexical and grammatical forms required for the application domain
NLG Choice IssuesArchitecture Communicative Goal Knowledge Base Discourse Planner Mechanisms for building Discourse Structures: - Text schemata; Rhetorical Relations Content Selection Surface Realizer Approaches: - Systemic Grammar; - Functional Unification Grammar Natural Language Output Microplanning Lexical Selection Context Selection Discourse Structure Sentence Structure -referring expressions -aggregation Output from DP Input to SR
Surface Realizer Produces ordered sequences of words as constrained by the rules of lexicon and grammar. Approaches: Systemic Grammar Functional Unification Grammar Treats language as resource for expressing meaning in context Represents sentences as collections of functions and maintains rules for mapping those functions onto explicit grammatical forms Expressed as a system networksystem network
Surface Realizer Produces ordered sequences or words as constrained by the rules of lexicon and grammar. Approaches: Systemic Grammar Functional Unification Grammar Treats language as resource for expressing meaning in context Represents sentences as collections of functions and maintains rules for mapping those functions onto explicit grammatical forms Expressed as a system networksystem network Builds generational grammar as a feature structure with potential alternations Then unifies it with input specification built using the same sort of feature structure Expressed as an attribute-value matrixattribute-value matrix
Surface Realizer Produces ordered sequences or words as constrained by the rules of lexicon and grammar. Approaches: Systemic Grammar Functional Unification Grammar Take input at different levels Treats language as resource for expressing meaning in context Represents sentences as collections of functions and maintains rules for mapping those functions onto explicit grammatical forms Expressed as a system networksystem network Both grammars use functional categorizations - input is functionally rather than syntactically specified support multiple levels that are entered recursively during the generation process Builds generational grammar as a feature structure with potential alternations Then unifies it with input specification built using the same sort of feature structure Expressed as an attribute-value matrixattribute-value matrix Input represented as a functional description:
NLG Choice IssuesArchitecture Communicative Goal Knowledge Base Discourse Planner Mechanisms for building Discourse Structures: - Text schemata; Rhetorical Relations Content Selection Surface Realizer Approaches: - Systemic Grammar; - Functional Unification Grammar Natural Language Output Microplanning Lexical Selection Context Selection Discourse Structure Sentence Structure -referring expressions -aggregation Output from DP Input to SR
Discourse Planner Keeps track of the focus and the local topic of discourse; considers relationships between sentences. Also responsible for content selection and lexical selection Mechanisms for building discourse structures: Text Schemata Useful if a discrete set of consistent patterns and expressions can be found and encoded May be represented as an augmented transition network Problems: - impractical when text calls for structural variety and richness of expression - resulting discourse structure includes no higher-level structure relating sentences together Rhetorical Relations Rhetorical Structure Theory – text organization based on relationships between parts of text RST relations: elaboration, contrast, condition, purpose, result, etc. Used when the text being generated calls for variation Can develop own schema based on a particular situation
NLG Choice IssuesArchitecture Communicative Goal Knowledge Base Discourse Planner Mechanisms for building Discourse Structures: - Text schemata; Rhetorical Relations Content Selection Surface Realizer Approaches: - Systemic Grammar; - Functional Unification Grammar Natural Language Output Microplanning Lexical Selection Context Selection Discourse Structure Sentence Structure -referring expressions -aggregation Output from DP Input to SR
Microplanning The link between the discourse planner output and the surface realizer input Two main areas of concern: Referring expressions -determine those aspects of an entity that should be used when referring to it in a particular context Aggregation - apportioning the content from the knowledge base into phrase, clause, and sentence-sized chunks
NLG Choice IssuesArchitecture Communicative Goal Knowledge Base Discourse Planner Mechanisms for building Discourse Structures: - Text schemata; Rhetorical Relations Content Selection Surface Realizer Approaches: - Systemic Grammar; - Functional Unification Grammar Natural Language Output Microplanning Lexical Selection Context Selection Discourse Structure Sentence Structure -referring expressions -aggregation Output from DP Input to SR