Planning Text for Advisory Dialogues: Capturing Intentional and Rhetorical Information

By Johanna D. Moore, Cécile Paris. 1993

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “The recent surge in explainable AI has not (yet) truly adopted the concept sociallyinteractive explanation, at least, relative to the first wave of explainable AI systems such as that by Cawsey [20] and Moore and Paris [134].”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “In a similar vein, both Hovy (1991) and Moore and Paris (1993) interpreted the relations of Rhetorical Structure Theory (Mann & Thompson, 1988) as operators for text planning.”
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue (2019, background): “…like CONTRAST, that have long been taken to be central to generating coherent discourse in tradi- 2The datasets and implementations can be found at https://github.com/facebookresearch/ TreeNLG. tional NLG (Mann and Thompson, 1988; Moore and Paris, 1993; Reiter and Dale, 2000; Stent et al., 2002).”
Individual and Domain Adaptation in Sentence Planning for Dialogue (2007, methodology): “Discourse planning algorithms can be characterized as: schema-based (McKeown, 1985; Kittredge, Korelsky, & Rambow, 1991); top-down algorithms using plan operators ( Moore & Paris, 1993 ); or bottom-up approaches that use, for example, constraint satisfaction algorithms (Marcu, 1996, 1997) or genetic”
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring? (2018, background): “sive settings (Scott and de Souza, 1990; Moore and Paris, 1993), in order to express discourse relations that hold between content items.”
Building applied natural language generation systems (1997, background): “See Hovy (1993) and Moore and Paris (1993) for descriptions of systems that use this approach.”
Generation and evaluation of user tailored responses in multimodal dialogue (2004, background): “…Cognitive Science, vol. 28, no. 5, pp. 811- 840. https://doi.org/10.1207/s15516709cog2805_8 Digital Object Identifier (DOI): 10.1207/s15516709cog2805_8 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Cognitive Science General…”
Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory (2001, background): “• Previous research has shown that RST trees can play a crucial role in building natural language generation systems (Hovy, 1993; Moore and Paris, 1993; Moore, 1995) and text summarization systems (Marcu, 2000); can be used to increase the naturalness of machine translation outputs (Marcu et al.…”
Inferring Strategies for Sentence Ordering in Multidocument News Summarization (2002, methodology): “Producing a good ordering of information is also a critical task for the generation community, which has extensively investigated the issue (McKeown, 1985; Moore & Paris, 1993; Hovy, 1993; Bouayad-Agha, Power, & Scott, 2000; Mooney, Carberry, & McCoy, 1990).”
Designs for explaining intelligent agents (2009, background): “Related efforts include formal models of explanatory discourse (Cawsey, 1992; Moore and Paris, 1993), empirical work on the use and effects of providing explanation facilities to intelligent agent users (Berry et al., 1995; Ye and Johnson, 1995; Dhaliwal and Benbasat, 1996), and design studies…”
Explainable Artificial Intelligence: a Systematic Review (2020, unknown): “Interactivity The capacity of an explanation system to reason about previous utterances both to interpret and answer users’ follow-up questions [80, 81] Interestingness The capacity of a method for explainability to facilitate the discovery of novel knowledge and to engage user’s attention [64, 82, ”
Survey of the State of the Art in Human Language Technology (2010, background): “An example of such a plan operator from the system of Moore and Paris (1993) is the following:”