Data-to-Text Generation Improves Decision-Making Under Uncertainty

Gkatzia, Dimitra; Lemon, Oliver; Rieser, Verena

doi:10.1109/mci.2017.2708998

Cited by 22 publications

(23 citation statements)

References 27 publications

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“…A similar experimentation in an intensive-care unit confirmed the utility of natural language descriptions for clinicians [5]. In a different domain, that is weather forecasts, the automatic generation of messages concerning uncertain weather data can improve the performances of the users participating to a simulation experiment [6].…”

Section: Introductionmentioning

confidence: 78%

“…(5) Starting from the recommendation elaborated by the Reasoner, the NLG service generates a simple explanation for the user in plain natural language. (6) The result provided by the NLG service is sent to the app by the DietManager: the user will see this final result on her/his smartphone. If the user wants to eat the dish, this information will be sent to the DietManager by the app, and the list of eaten food will be updated.…”

Section: The Madiman Architecturementioning

confidence: 99%

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Building a Persuasive Virtual Dietitian

Anselma

Mazzei

2020

Informatics

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This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results.

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Section: Introductionmentioning

confidence: 78%

Section: The Madiman Architecturementioning

confidence: 99%

Building a Persuasive Virtual Dietitian

Anselma

Mazzei

2020

Informatics

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“…Besides, Gkatzia et al (2017) and Portet et al (2009) proposed non-neural language generation models for the data-to-text task with higher controllability on the output. They assumed that the important contents and their descriptions are determined primarily by experts, and their models do not allow users to select the contents directly.…”

Section: Related Studymentioning

confidence: 99%

Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels

Aoki¹,

Miyazawa²,

Ishigaki³

et al. 2019

Proceedings of the 12th International Conference on Natural Language Generation

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We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, since it differs from users to users what they are interested in, it is necessary to develop a method to generate various summaries according to users' requests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei 225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation. Experiments show both models using additional information of target document achieved higher performance in terms of BLEU and human evaluation. We found that human-designed topic labels are superior to extracted keywords in terms of controllability.

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“…In the realm of context-aware services and interactive applications, Natural Language Generation (NLG) involving maps in combination with meteorological data is subject to active field research (Ramos-Soto et al, 2015). Automatically generating recommendations consisting of both text and figures can help users in making decisions while providing personalized services (Gkatzia et al, 2017). Furthermore, it is not just an issue of giving a suitable recommendation according to the user's context (Mocholi et al, 2012), but also to design content generators in such a way that the artificial intelligence associated to the service is better considered in terms of being explainable, accountable and intelligible (Abdul et al, 2018;Alonso et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Supporting Content Design with an Eye Tracker: The Case of Weather-based Recommendations

Catalá¹,

Alonso²,

Bugarín

2018

Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)

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Designing content output for weatheraware services based on domain experts can sometimes be arduous due to their limited availability and the amount and complexity of information considered in explaining their recommendations. As an initial step in our work towards generating recommendations that are acceptable and readable, our methodology involving an eye tracker attempts to simplify and capture more valuable data in early design stages. Our pilot study explored which information in weather-based recommendations seemed to be more useful to support users decision making. The results suggest that interactive content could be deployed based on the relevance of informational items and both graphical points of interest and legends could help in delivering content more efficiently.

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Data-to-Text Generation Improves Decision-Making Under Uncertainty

Cited by 22 publications

References 27 publications

Building a Persuasive Virtual Dietitian

Building a Persuasive Virtual Dietitian

Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels

Supporting Content Design with an Eye Tracker: The Case of Weather-based Recommendations

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