RI: EAGER: Exploratory Research on Acquiring and Adapting Sentence Planning Resources for Generating with Discourse Combinatory Categorial Grammar

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Natural Language Generation (NLG) systems aim to improve the accessibility and impact of information by turning data into coherent and fluent text or speech, automatically. Developing high-quality NLG systems, however, remains a difficult and costly undertaking, in large part because bridging the gap between content planning and surface realization---a task known as \textit{sentence planning}---continues to require extensive knowledge engineering.This Early Grant for Exploratory Research investigates ways of bridging this gap by employing machine learning together with Discourse Combinatory Categorial Grammar (DCCG). Using a restaurant recommendation application as a proof-of-concept, the project explores methods of (1) adapting previous work on acquiring lexicalized grammar entries for semantic parsing to learn mappings from domain-general semantic dependency representations to application-specific representations of messages; (2) extending the approach to learn rules for combining messages; (3) employing the acquired resources to map content plans to disjunctive logical forms (DLFs), which compactly specify the range of possible realizations of the selected content; and (4) improving the efficiency of realizing DLFs with OpenCCG through grammar specialization.The project will evaluate the success of these novel methods and assess the portability of the approach. By demonstrating methods for radically simplifying the construction of NLG systems, the project promises to transform the way NLG systems are built, from today's knowledge-intensive approach to one that relies primarily on assembling a parallel corpus of input-output pairs. Ultimately, it will facilitate the development of generation components in data-to-text systems as well as dialogue systems, including ones for the visually impaired.