pwc-archive/papers-with-abstracts huggingface dataset:
Towards Universal Semantic Representation
Authors: Huaiyu Zhu, Yunyao Li, Laura Chiticariu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation, Natural Language Understanding, Semantic Role Labeling

Natural language understanding at the semantic level and independent of language variations is of great practical value. Existing approaches such as semantic role labeling (SRL) and abstract meaning representation (AMR) still have features related to the peculiarities of the particular language. In this work we describe various challenges and possible solutions in designing a semantic representation that is universal across a variety of languages.
A Dependency Structure Annotation for Modality
Authors: Meagan Vigus, Jens E. L. Van Gysel, William Croft
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper presents an annotation scheme for modality that employs a dependency structure. Events and sources (here, conceivers) are represented as nodes and epistemic strength relations characterize the edges. The epistemic strength values are largely based on Saur{\'\i} and Pustejovsky{'}s (2009) FactBank, while the dependency structure mirrors Zhang and Xue{'}s (2018b) approach to temporal relations. Six documents containing 377 events have been annotated by two expert annotators with high levels of agreement.
Augmenting Abstract Meaning Representation for Human-Robot Dialogue
Authors: Claire Bonial, Lucia Donatelli, Stephanie M. Lukin, Stephen Tratz, Ron artstein, David Traum, Clare Voss
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.
Composing a Picture Book by Automatic Story Understanding and Visualization
Authors: Xiaoyu Qi, Ruihua Song, Chunting Wang, Jin Zhou, Tetsuya Sakai
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Event Extraction, Sentence

Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.
``My Way of Telling a Story'': Persona based Grounded Story Generation
Authors: Ch, Khyathi u, Shrimai Prabhumoye, Ruslan Salakhutdinov, Alan W. black
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Story Generation, Visual Storytelling

Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas. Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona. In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation. We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction. To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand. In our experiments we use five different personas to guide the generation process. We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.
Using Functional Schemas to Understand Social Media Narratives
Authors: Xinru Yan, Aakanksha Naik, Yohan Jo, Carolyn Rose
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Classification, General Classification, text-classification, Text Classification

We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4{\%} on average. We also observe that these schemas serve as lenses that reveal community norms.
A Hybrid Model for Globally Coherent Story Generation
Authors: Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, Asad Sayeed
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: model, Story Generation, Text Generation

Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite data-demanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.
Guided Neural Language Generation for Automated Storytelling
Authors: Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara Martin, Mark Riedl
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence, Story Generation, Text Generation

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.
An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling
Authors: Evgeny Kim, Roman Klinger
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Centrality of emotion for the stories told by humans is underpinned by numerous studies in literature and psychology. The research in automatic storytelling has recently turned towards emotional storytelling, in which characters{'} emotions play an important role in the plot development (Theune et al., 2004; y Perez, 2007; Mendez et al., 2016). However, these studies mainly use emotion to generate propositional statements in the form {``}A feels affection towards B{''} or {``}A confronts B{''}. At the same time, emotional behavior does not boil down to such propositional descriptions, as humans display complex and highly variable patterns in communicating their emotions, both verbally and non-verbally. In this paper, we analyze how emotions are expressed non-verbally in a corpus of fan fiction short stories. Our analysis shows that stories written by humans convey character emotions along various non-verbal channels. We find that some non-verbal channels, such as facial expressions and voice characteristics of the characters, are more strongly associated with joy, while gestures and body postures are more likely to occur with trust. Based on our analysis, we argue that automatic storytelling systems should take variability of emotion into account when generating descriptions of characters{'} emotions.
Narrative Generation in the Wild: Methods from NaNoGenMo
Authors: Judith van Stegeren, Mari{\"e}t Theune
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Text Generation

In text generation, generating long stories is still a challenge. Coherence tends to decrease rapidly as the output length increases. Especially for generated stories, coherence of the narrative is an important quality aspect of the output text. In this paper we examine how narrative coherence is attained in the submissions of NaNoGenMo 2018, an online text generation event where participants are challenged to generate a 50,000 word novel. We list the main approaches that were used to generate coherent narratives and link them to scientific literature. Finally, we give recommendations on when to use which approach.
Lexical concreteness in narrative
Authors: Michael Flor, Swapna Somasundaran
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Relation

This study explores the relation between lexical concreteness and narrative text quality. We present a methodology to quantitatively measure lexical concreteness of a text. We apply it to a corpus of student stories, scored according to writing evaluation rubrics. Lexical concreteness is weakly-to-moderately related to story quality, depending on story-type. The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.
A Simple Approach to Classify Fictional and Non-Fictional Genres
Authors: Mohammed Rameez Qureshi, Sidharth Ranjan, Rajakrishnan Rajkumar, Kushal Shah
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: feature selection, Sentence

In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre. For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length). Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text. As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant. Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.
Personality Traits Recognition in Literary Texts
Authors: Daniele Pizzolli, Carlo Strapparava
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning

Interesting stories often are built around interesting characters. Finding and detailing what makes an interesting character is a real challenge, but certainly a significant cue is the character personality traits. Our exploratory work tests the adaptability of the current personality traits theories to literal characters, focusing on the analysis of utterances in theatre scripts. And, at the opposite, we try to find significant traits for interesting characters. The preliminary results demonstrate that our approach is reasonable. Using machine learning for gaining insight into the personality traits of fictional characters can make sense.
Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events
Authors: Anietie Andy, Derry Tanti Wijaya, Chris Callison-Burch
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.
Prediction of a Movie's Success From Plot Summaries Using Deep Learning Models
Authors: You Jin Kim, Yun Gyung Cheong, Jung Hoon Lee
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

As the size of investment for movie production grows bigger, the need for predicting a movie{'}s success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie{'}s performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work.
Subversive Toxicity Detection using Sentiment Information
Authors: Eloi Brassard-Gourdeau, Richard Khoury
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Authors: Fan Yang, Xiaochang Peng, Gargi Ghosh, Reshef Shilon, Hao Ma, Eider Moore, Goran Predovic
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: General Classification

Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users{'} interactions on today{'}s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.
Detecting harassment in real-time as conversations develop
Authors: Wessel Stoop, Florian Kunneman, Antal Van den Bosch, Ben Miller
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning

We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.
Automated Identification of Verbally Abusive Behaviors in Online Discussions
Authors: Srecko Joksimovic, Ryan S. Baker, Jaclyn Ocumpaugh, Juan Miguel L. Andres, Ivan Tot, Elle Yuan Wang, Shane Dawson
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.
Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Authors: Muhammad Okky Ibrohim, Indra Budi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abuse Detection, Abusive Language, Hate Speech Detection, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.
The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit's Quarantine Policy
Authors: Qinlan Shen, Carolyn Rose
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abusive Language

Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users{'} ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.
Pay ``Attention'' to your Context when Classifying Abusive Language
Authors: Tuhin Chakrabarty, Kilol Gupta, Smar Muresan, a
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abuse Detection

The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.
Challenges and frontiers in abusive content detection
Authors: Bertie Vidgen, Alex Harris, Dong Nguyen, Rebekah Tromble, Scott Hale, Helen Margetts
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abuse Detection

Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss potential solutions. We also highlight ways in which social scientific insights can advance research. We discuss the lack of support given to researchers working with abusive content and provide guidelines for ethical research.