Explaining Violation Traces with Finite State Natural Language Generation Models
By Gordon J. Pace, M. Rosner. 2014
3 citations recorded by the source corpus.
An essential element of any verification technique is that of identifying and communicating to the user, system behaviour which leads to a deviation from the expected behaviour. Such behaviours are typically made available as long traces of system actions which would benefit from a natural language explanation of the trace and especially in the context of business logic level specifications. In this paper we present a natural language generation model which can be used to explain such traces. A key idea is that the explanation language is a CNL that is, formally speaking, regular language susceptible transformations that can be expressed with finite state machinery. At the same time it admits various forms of abstraction and simplification which contribute to the naturalness of explanations that are communicated to the user.
Subfields: Information Systems, Software
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Citations in the corpus
- Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…Barzilay, McCullough, Rambow, DeCristofaro, Korelsky, and Lavoie, 1998; Chai, Fang, Liu, and She (2016); Dodson, Mattei, and Goldsmith, 2011; Henrion and Druzdzel (1990ab), Lazaridou, Peysakhovich, and Baroni, 2017; Neihaus and Young, 2014; Pace and Rosner, 2014; Papamichail, and French, 2003).”
- Explanation and Justification in Machine Learning : A Survey Or (2017, methodology): “For example, [Pace and Rosner, 2014] produce explanations of user interactions with a software system, intended for administrators, while [Gkatzia et al. , 2016] explain how a weather forecast was produced and show that it helps readers decide whether or not to believe the forecast.”