A Theme-Rewriting Approach for Generating Algebra Word Problems

Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\it rewriting} that edits existing human-authored narratives to change their theme without changing the underlying story. We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught. Our rewriting method uses a two-stage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence. Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines. We also release a new dataset of human-authored rewrites of math word problems in several themes.Comment: To appear EMNLP 201

Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc

NAREOR: The Narrative Reordering Problem

Many implicit inferences exist in text depending on how it is structured that can critically impact the text's interpretation and meaning. One such structural aspect present in text with chronology is the order of its presentation. For narratives or stories, this is known as the narrative order. Reordering a narrative can impact the temporal, causal, event-based, and other inferences readers draw from it, which in turn can have strong effects both on its interpretation and interestingness. In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot. We present a dataset, NAREORC, with human rewritings of stories within ROCStories in non-linear orders, and conduct a detailed analysis of it. Further, we propose novel task-specific training methods with suitable evaluation metrics. We perform experiments on NAREORC using state-of-the-art models such as BART and T5 and conduct extensive automatic and human evaluations. We demonstrate that although our models can perform decently, NAREOR is a challenging task with potential for further exploration. We also investigate two applications of NAREOR: generation of more interesting variations of stories and serving as adversarial sets for temporal/event-related tasks, besides discussing other prospective ones, such as for pedagogical setups related to language skills like essay writing and applications to medicine involving clinical narratives.Comment: Accepted to AAAI 202

When Beauty Goes to Sleep: an analysis of the symbolism behind the sleeping beauty tale

Approaching the world of the fairy tale as an adult, one soon realizes that things are not what they once seemed during story time in bed. Something that once appeared so innocent and simple can become rather complex when digging into its origin. A kiss, for example, can mean something else entirely. I can clearly remember my sister, who is ten years older than I am, telling me that the fairy tales I was told had a mysterious hidden meaning I could not understand. I was probably 9 or 10 when she told me that the story of Sleeping Beauty, which I used to love so much in Disney’s rendering, was nothing more than the story of an adolescent girl, with all the necessary steps needed to become a woman, the bleeding of menstruation and the sexual awakening - even though she did not really put it in these terms. This shocking news troubled me for a while, so much so that I haven’t watched that movie since. But in reality it was not fear that my sister had implanted in me: it was curiosity, the feeling that I was missing something terribly important behind the words and images. But it was not until last year during my semester abroad in Germany, where I had the chance to take a very interesting English literature seminar, that I fully understood what I had been looking for all these years. Thanks to what I learned from the work of Bruno Bettelheim, Jack Zipes, Vladimir Propp, and many other authors that wrote extensively about the subject, I feel I finally have the right tools to really get to know this fairy tale. But what I also know now is that the message behind fairy tales is not to be searched for behind only one version: on the contrary, since they come from oral traditions and their form was slowly shaped by centuries of recountals and retellings, the more one digs, the more complete the understanding of the tale will be. I will therefore look for Sleeping Beauty’s hidden meaning by looking for the reason why it did stick so consistently throughout time. To achieve this goal, I have organized my analysis in three chapters: in the first chapter, I will analyze the first known literary version of the tale, the French Perceforest, and then compare it with the following Italian version, Basile’s Sun, Moon, and Talia; in the second chapter, I will focus on the most famous and by now classical literary versions of Sleeping Beauty, La Belle Au Bois Dormant, written by the Frenchman, Perrault, and the German Dornröschen, recorded by the Brothers Grimm’s; finally, in the last chapter, I will analyze Almodovar’s film Talk to Her as a modern rewriting of this tale, which after a closer look, appears closely related to the earliest version of the story, Perceforest

Computational Cognitive Models of Summarization Assessment Skills

This paper presents a general computational cognitive model of the way a summary is assessed by teachers. It is based on models of two subprocesses: determining the importance of sentences and guessing the cognitive rules that the student may have used. All models are based on Latent Semantic Analysis, a computational model of the representation of the meaning of words and sentences. Models' performances are compared with data from an experiment conducted with 278 middle school students. The general model was implemented in a learning environment designed for helping students to write summaries

Towards a graph-based model of computer games

This paper proposes a new holistic approach to a formal model of computer games. The story and structure of a computer game is represented by a hierarchical layered graph, meanwhile the way that the game is played – by graph transformations. This approach enables comparative description of different games, analysis of dependencies between a game structure and players’ strategies, automatic gameplay generation, and switching from single- to multi­player mode

Auctor in Fabula: Umberto Eco and the Intentio of Foucault\u27s Pendulum

Umberto Eco’s 1988 novel Foucault’s Pendulum weaves together a wide range of philosophical and literary threads. Many of these threads find their other ends in Eco’s nonfiction works, which focus primarily on the question of interpretation and the source of meaning. The novel, which follows three distinctly overinterpretive characters as they descend into ruin, has been read by some as a retraction or parody of Eco’s own position. However, if Foucault’s Pendulum is indeed polemical, it must be taken as an argument against the mindset which Eco has termed the “hermetic”. Through an examination of his larger theoretical body, including its themes and intellectual heritage, it will be seen that Eco preserves his philosophical consistency across his fictive and non-fictive work

Adapting Automatic Summarization to New Sources of Information

English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media, and foreign-language news articles are accessible to, if not immediately understandable by, English-speaking users. This thesis focuses on developing automatic summarization techniques for these new sources of information. We focus on summarizing two specific new sources of information: personal narratives, first-person accounts of exciting or unusual events that are readily found in blog entries and other social media posts, and non-English documents, which must first be translated into English, often introducing translation errors that complicate the summarization process. Personal narratives are a very new area of interest in natural language processing research, and they present two key challenges for summarization. First, unlike many news articles, whose lead sentences serve as summaries of the most important ideas in the articles, personal narratives provide no such shortcuts for determining where important information occurs in within them; second, personal narratives are written informally and colloquially, and unlike news articles, they are rarely edited, so they require heavier editing and rewriting during the summarization process. Non-English documents, whether news or narrative, present yet another source of difficulty on top of any challenges inherent to their genre: they must be translated into English, potentially introducing translation errors and disfluencies that must be identified and corrected during summarization. The bulk of this thesis is dedicated to addressing the challenges of summarizing personal narratives found on the Web. We develop a two-stage summarization system for personal narrative that first extracts sentences containing important content and then rewrites those sentences into summary-appropriate forms. Our content extraction system is inspired by contextualist narrative theory, using changes in writing style throughout a narrative to detect sentences containing important information; it outperforms both graph-based and neural network approaches to sentence extraction for this genre. Our paraphrasing system rewrites the extracted sentences into shorter, standalone summary sentences, learning to mimic the paraphrasing choices of human summarizers more closely than can traditional lexicon- or translation-based paraphrasing approaches. We conclude with a chapter dedicated to summarizing non-English documents written in low-resource languages – documents that would otherwise be unreadable for English-speaking users. We develop a cross-lingual summarization system that performs even heavier editing and rewriting than does our personal narrative paraphrasing system; we create and train on large amounts of synthetic errorful translations of foreign-language documents. Our approach produces fluent English summaries from disdisfluent translations of non-English documents, and it generalizes across languages