For data-to-text tasks in Natural Language Generation (NLG), researchers are often faced with choices about the right words to express phenomena seen in the data.One common phenomenon centers around the description of trends between two data points and selecting the appropriate verb to express both the direction and intensity of movement.Our research shows that rather than simply selecting the same verbs again and again, variation and naturalness can be achieved by quantifying writers' patterns of usage around verbs.

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.