An xAI Approach for Data-to-Text Processing with ASP
This work addresses data-to-text generation for AI applications, but it appears incremental as it builds on existing logic-based methods with specific enhancements.
The paper tackles the problem of generating natural language text from data series by addressing challenges in identifying key descriptive elements and ensuring accuracy, coherence, and synthesis control, resulting in a framework using ASP/Python programs that provide explicit control and proven optimal solutions.
The generation of natural language text from data series gained renewed interest among AI research goals. Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input. Main challenges of such approaches are the proper identification of "what" to say (the key descriptive elements to be addressed in the data) and "how" to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis. This paper presents a framework that is compliant with xAI requirements. In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions. The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules. The generation of natural language descriptions' structure is also managed by logic rules.