Towards Generating Stylistic Dialogues for Narratives using Data-Driven Approaches

Recently, there has been a renewed interest in generating dialogues for narratives. Within narrative dialogues, their structure and content are essential, though style holds an important role as a mean to express narrative dialogue through telling stories. Most existing approaches of narrative dialogue generation tend to leverage hand-crafted rules and linguistic-level styles, which lead to limitations in their expressivity and issues with scalability. We aim to investigate the potential of generating more stylistic dialogues within the context of narratives. To reach this, we propose a new approach and demonstrate its feasibility through the support of deep learning. We also describe this approach using examples, where story-level features are analysed and modelled based on a classification of characters and genres

Influence of Personality-based Features for Dialogue Generation in Computational Narratives

In this paper, we present an approach for generating dialogues for characters within the context of computational narratives using personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models. The modelling of characters’ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of characters’ personality traits across different narrative genres, demonstrating variability in the resulting dialogue

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

A survey of comics research in computer science

Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, human-computer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks

Styling the Future. A philosophical account of scenarios & design

Since the end of the 1980s – the Decade of Style (Mort, 1996) – the value of style in design has fallen. Recent times (Whicher et al., 2015) see a focus on style as a sign of design’s immaturity, while a more mature design should be attending to process, strategy and policy creation. Design Thinking has been enjoying its success in the same spirit, where it is championed (Brown, 2008; Martin, 2009; Neumeier, 2009) as a way of taking design away from its early stage as ‘mere’ styling, towards the more thoughtful, serious matters of business. The philosopher Gilles Deleuze is of a different mind however. ‘Style,’ he writes (1995, p.31), ‘amounts to innovation.’ For us this engages not only a rethinking of design practice in particular, but also a reconsideration of the guiding principles of scenario planning. Deleuze’s thought entails the opportunity for styling to be an act that participates in driving all creativity towards making a successful future impact (Flynn & Chatman, 2004; Cox, 2005). A philosophical disruption of current design and scenarios orthodoxies offers a way of considering that style has a key role in the production of the future. Here, then, we will investigate the creative, even innovative, opportunities that emerge from a reworking of the value of style that comes from a critique of Design Thinking, a perspective on future-thinking (especially scenario planning (e.g. Schwartz, 1991; Li, 2014; Ramírez & Selin, 2014), but also some work from organisation and management studies (e.g. Tsoukas, 2005a, 2005b)), and an encounter with philosophy (particularly the work of Deleuze & Guattari (1984, 1987, 1994). We will highlight the affective capacities of style – in design and scenarios, both as creative constructing of futures – by way of creatively accessing uncertainty, complexity and indeterminacy in the production of strategic maps for living (both individuals and organisations)

Stylistic Dialogue Generation Based on Character Personality in Narrative Films.

Traditional narrative systems consist of two steps of process, story generation and discourse generation. However, many interactive systems make more effort on story generation rather than discourse generation. For discourse generation, dialogue is an important way used to unfold story and reveal characters in stories, and it is reasonable to expand the capability of narrative system by exploring the potential of dialogue generation in narratives. Also, Recent research in conditional dialogue generation is mostly focusing on the context of natural conversation generation with speakers’ profile information. While incorporating the styles that relevant to narratives is yet to be widely investigated. According to the research made, in this document, we propose an approach using a pre-trained language model, in order to explore the potential of generating dialogues with embedded narrative-related features within the context of narrative films. In this approach, three different embedding methods are leveraged to incorporate Big-Five personalities of characters into transformer-based neural networks, training on a new corpus, which is created and well-parsed from screenplays. We conduct experiments using both automatic metrics and human evaluation to measure the quality of the generated dialogue and personality identification accuracy. All the dialogues for evaluation and analysis are generated with settings of the perspectives of embedding method, personality trait, personality level, and film genre, which is to explore the impact of different setting on dialogue generation with additional narrative-related styles. According to the automatic experimental results, we demonstrate that our approach is able to generate dialogues with increased variety. Also overall, the generated dialogues are able to correctly reflect the given target personality. We also conduct three user studies for evaluate dialogues with human judgements. In the first and the second user study, we evaluate the dialogues generated with film- level personality using CTE (Combined Textual Embedding) embedding method. The results show that human participants are inclined to perceive one extreme end of each personality trait. In the third user study, we evaluate generated dialogues with all setting combinations synthetically. Overall, the results show that target personalities can be identified with various degrees of accuracy. Also, a negative correlation between personality identification accuracy and dialogue quality is observed. In this thesis, we propose a new approach for stylistic dialogue generation and demonstrate its effectiveness. We believe the observations and discoveries could be a start and a tryout to apply deep learning technique and big data to boost narrative dialogue generation. And we also believe that our research can be applied in plenty of potential scenarios, such as helping the authors creating huge amount of conversations between different characters by popping utterance options corresponding to the character settings

A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling

Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesop’s Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Tales’ automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters