Generating and evaluating evaluative arguments

Carenini, Giuseppe; Moore, Johanna D.

doi:10.1016/j.artint.2006.05.003

Cited by 113 publications

(98 citation statements)

References 31 publications

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“…However, how to orchestrate a reasonable set of recomputations effectively is ambitious. A summmary of the reasoning techniques discussed, in terms of major weaknesses and meaures to potentially overcome them is given in Table 1 4 Presentation Methods Explanation presentation needs good sentence planning, including aggregation (Di , and argumentation organization (Carenini, Moore, 2006). In addition, having a good command of explicitness and implicitness in presentation is of great importance in this genre , even more prominently in varions versions of the Digital Aristotle (Porter, 2007).…”

Section: Neural Networkmentioning

confidence: 99%

Requirements for Conceptual Representations of Explanations and How Reasoning Systems Can Serve Them

Horacek¹

2017

Proceedings of the 1st Workshop on Explainable Computational Intelligence (XCI 2017)

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Explanations of solutions produced by reasoning systems in ever growing complexity become increasingly interesting, which is particularly challenging in view of fundamental differences between human and machine representation and problem-solving methods. In this paper, we formulate requirements for conceptual representations that are adequate for producing human-oriented explanations, and we discuss how some reasoning mechanisms can serve them or can possibly be adapted to do so. This examination is intended to state in what ways reasoning systems can potentially support explanation generation, and where technology-justified limitations have to be accepted.

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Section: Neural Networkmentioning

confidence: 99%

Requirements for Conceptual Representations of Explanations and How Reasoning Systems Can Serve Them

Horacek¹

2017

Proceedings of the 1st Workshop on Explainable Computational Intelligence (XCI 2017)

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“…Elsewhere in the field of natural language generation, there is work that seeks to generate persuasive text (Carenini and Moore, 2006;Reiter et al, 2003;Rosenfeld and Kraus, 2016), which is a logical first step towards creating an automated debate agent. One major deficiency of existing work in this area is its assessment of how convincing (or compelling) a piece of text is; the approaches use theory-driven models of persuasion, rather than being empirically motivated.…”

Section: Introductionmentioning

confidence: 99%

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

Potash¹,

Rumshisky²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.

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“…For instance, the content selection strategy used in STOP, a system for generating smoking cessation letters, is quite domain specific as it refers to psychological knowledge about addictive behavior and smoking [44]. In contrast, the content selection strategy used by the GEA system does not rely on any domain-specific knowledge (as we will see later in this section) and can be therefore applied in any domain [10]. Because of their generality, in this section we focus on strategies that are primarily domainindependent.…”

Section: Approaches Based On Abstract Informationmentioning

confidence: 99%

“…By setting this threshold to different values it is possible to generate, in a principled way, arguments that con- tain different amounts of user-tailored relevant content, as shown in Fig. 13.1 (see [10] for details).…”

Section: The Generator Of Evaluative Arguments (Gea)mentioning

confidence: 99%

“…For instance, one application of the GEA system [10] generates usertailored arguments on whether the user will like/dislike a given house by relying on a model specifying what aspects of a house the user cares most about (e.g., location, amenities). The PRACMA system [29] also employs a model of user preferences to tailor its description of an individual recommended item (e.g., a car) by focusing on the aspect (e.g., price) that will have the largest impact on the user's overall evaluation of that item.…”

Section: Approaches Based On Abstract Informationmentioning

confidence: 99%

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Adaptive Content Presentation for the Web

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Abstract. In this chapter we describe techniques for adaptive presentation of content on the Web. We first describe techniques to select and structure the content deemed to be most relevant for the current user in the current interaction context. We then illustrate approaches that deal with the problem of how to adaptively deliver this content. IntroductionPrevious chapters in this book have described types of adaptation for Web-based systems that include adaptive navigation support (see Chapter 8 of this book [8]), adaptive search (see Chapter 6 of this book [39]) and personalized recommendation of items of interest (see Chapters 9 [47], 10 [42], 11 [49], and 12 [9] of this book). In this chapter, we will focus on an additional type of adaptation widely known as adaptive presentation of content: how to present Web-based content in a manner that best suits individual users' needs. This type of adaptation involves determining, based on the user and context, what information the system should present and how the information should be organized and displayed. While adaptive presentation of content can serve many purposes, as we will demonstrate throughout the chapter, it can also complement several of the adaptation types discussed in previous chapters. For instance, the content of Web pages pointed to by a tailored link in a system that provides adaptive navigation support (Chapter 8 of this book [8]), or returned by adaptive search (Chapter 6 of this book [39]), can be modified to highlight the parts that are more interesting for the current user. Similarly, the description of the items returned by a recommender system (see Chapters 9 [47], 10 [42], 11 [49], and 12 [9] of this book) and can be adapted to play up the items' features that are more relevant to the user's needs, or changed to be more suitable to the user's level of familiarity with the items.The focus of this chapter will be on computational techniques necessary to provide the user with a tailored presentation of content, rather than implementation details and technologies. Also, the chapter is not limited to techniques currently used in adaptive Web-based applications. It aims to suggest areas of future research by discussing alternative approaches that have a strong potential to augment the set of existing techniques for adaptive presentation on the Web. The process of adapting content to specific user needs comprises two sub processes: content adaptation and presentation. Content adaptation involves deciding what content is most relevant to the current user and how to structure this content in a coherent way, before presenting it to the user. The second sub process of content presentation involves deciding how to most effectively adapt the presentation of the selected content to the user.The chapter is structured as follows. In section 13.2, we address techniques for content adaptation. Although traditionally these techniques required the existence of pre-crafted versions of the relevant content, new techniques are emerging which can automatically...

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Generating and evaluating evaluative arguments

Cited by 113 publications

References 31 publications

Requirements for Conceptual Representations of Explanations and How Reasoning Systems Can Serve Them

Requirements for Conceptual Representations of Explanations and How Reasoning Systems Can Serve Them

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

Adaptive Content Presentation for the Web

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