Fine-Grained Control of Sentence Segmentation and Entity Positioning in Neural NLG

International audienceThe move from pipeline Natural Language Generation (NLG) approaches to neural end-to-end approaches led to a loss of control in sentence planning operations owing to the conflation of intermediary micro-planning stages into a single model. Such control is highly necessary when the text should be tailored to respect some constraints such as which entity to be mentioned first, the entity position, the complexity of sentences, etc. In this paper, we introduce fine-grained control of sentence planning in neural data-to-text generation models at two levels-realization of input entities in desired sentences and realization of the input entities in the desired position among individual sentences. We show that by augmenting the input with explicit position identi-fiers, the neural model can achieve a great control over the output structure while keeping the naturalness of the generated text intact. Since sentence level metrics are not entirely suitable to evaluate this task, we used a metric specific to our task that accounts for the model's ability to achieve control. The results demonstrate that the position identifiers do constraint the neural model to respect the intended output structure which can be useful in a variety of domains that require the generated text to be in a certain structure

SYNTHNOTES: TOWARDS SYNTHETIC CLINICAL TEXT GENERATION

SynthNotes is a statistical natural language generation tool for the creation of realistic medical text notes for use by researchers in clinical language processing. Currently, advancements in medical analytics research face barriers due to patient privacy concerns which limits the numbers of researchers who have access to valuable data. Furthermore, privacy protections restrict the computing environments where data can be processed. This often adds prohibitive costs to researchers. The generation method described here provides domain-independent statistical methods for learning to generate text by extracting and ranking templates from a training corpus. The primary contribution in this work is automating the process of template selection and generation of text through classic machine learning methods. SynthNotes removes the need for human domain experts to construct templates, which can be time intensive and expensive. Furthermore, by using machine learning methods, this approach leads to greater realism and variability in the generated notes than could be achieved through classical language generation methods

Generating Tailored, Comparative Descriptions with Contextually Appropriate Intonation

Generating responses that take user preferences into account requires adaptation at all levels of the generation process. This article describes a multi-level approach to presenting user-tailored information in spoken dialogues which brings together for the first time multi-attribute decision models, strategic content planning, surface realization that incorporates prosody prediction, and unit selection synthesis that takes the resulting prosodic structure into account. The system selects the most important options to mention and the attributes that are most relevant to choosing between them, based on the user model. Multiple options are selected when each offers a compelling trade-off. To convey these trade-offs, the system employs a novel presentation strategy which straightforwardly lends itself to the determination of information structure, as well as the contents of referring expressions. During surface realization, the prosodic structure is derived from the information structure using Combinatory Categorial Grammar in a way that allows phrase boundaries to be determined in a flexible, data-driven fashion. This approach to choosing pitch accents and edge tones is shown to yield prosodic structures with significantly higher acceptability than baseline prosody prediction models in an expert evaluation. These prosodic structures are then shown to enable perceptibly more natural synthesis using a unit selection voice that aims to produce the target tunes, in comparison to two baseline synthetic voices. An expert evaluation and f0 analysis confirm the superiority of the generator-driven intonation and its contribution to listeners' ratings

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

Empirical Insights Into Short Story Draft Construction

Existing cognitive models of narrative creation provide accounts for story invention that, while useful, are too high level to be directly applied to formal systems like computational models of narrative generation. Inversely, existing automatic story generation systems that try to implement cognitive models can only rely on approximations to the general concepts these models describe. In order to provide insight to ll the gap between these two approaches, we have conducted a study in which human participants would invent and write short stories while re ecting on their thoughts out loud. The sessions and the analysis of the recordings was designed so that we could observe which speci c modi cations the participants apply to their story drafts, with the intention to inform the process of creating computational systems based on cognitive descriptions of the narrative creation process. After running the experiments, annotating the videos and analysing the output, we have concluded that there are a number of common modi cations that humans tend to apply to a newly created draft, and that this information can be used to the development of storytelling systems

Modeling a Conversational Agent using BDI Framework

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Automating Abell's theory of comparative narratives

The purpose of this thesis is to demonstrate the progress that has been made towards the goal of producing a prototype computer model of Abell's Theory of Comparative Narratives, and subsequently, designing metrics to rigorously measure Abell's concept of 'closeness' of texts. The production of such a model does not simply involve the mechanical (though distinctly non-trivial) transference of Abell's theory from paper to machine; various facets of the theory are not of a sufficiently high specification for a computer model and the fulfilment of such a computer model requires attention to these areas, specifically: i) a repeatable method of comparing the structures of individual events; ii) a consistent procedure of comparing the overall structure of a pair of texts, following on from Abell's basic concept of paths of social determination. iii) metrics to demonstrate that the solutions proposed do indeed address the shortcomings of Abell's theory. In order to preserve the qualitative nature of the theory and to demonstrate its potential real-world uses, the computer model attempts to avoid complex mathematics as far as possible and to produce transparent, non-expert results