Aadit Vyas

TL;DR: The dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing.

TL;DR: The DART dataset as mentioned in this paper is a large dataset for open-domain structured data record to text generation, which consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions.

TL;DR: ESRIT is a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events using a data-to-text approach and learns to generate explanations of how the physical simulation will causally evolve.

TL;DR: The authors propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events using a data-to-text approach, which learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable description.