pwc-archive/papers-with-abstracts huggingface dataset:
An Analysis of the Effect of Emotional Speech Synthesis on Non-Task-Oriented Dialogue System
Authors: Yuya Chiba, Takashi Nose, Taketo Kase, Mai Yamanaka, Akinori Ito
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue Management, Emotional Speech Synthesis, Management, Speech Synthesis, Spoken Dialogue Systems, Task-Oriented Dialogue Systems

This paper explores the effect of emotional speech synthesis on a spoken dialogue system when the dialogue is non-task-oriented. Although the use of emotional speech responses have been shown to be effective in a limited domain, e.g., scenario-based and counseling dialogue, the effect is still not clear in the non-task-oriented dialogue such as voice chatting. For this purpose, we constructed a simple dialogue system with example- and rule-based dialogue management. In the system, two types of emotion labeling with emotion estimation are adopted, i.e., system-driven and user-cooperative emotion labeling. We conducted a dialogue experiment where subjects evaluate the subjective quality of the system and the dialogue from the multiple aspects such as richness of the dialogue and impression of the agent. We then analyze and discuss the results and show the advantage of using appropriate emotions for the expressive speech responses in the non-task-oriented system.
Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue
Authors: Zahra Rahimi, Diane Litman
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

This paper proposes a new weighting method for extending a dyad-level measure of convergence to multi-party dialogues by considering group dynamics instead of simply averaging. Experiments indicate the usefulness of the proposed weighted measure and also show that in general a proper weighting of the dyad-level measures performs better than non-weighted averaging in multiple tasks.
Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model
Authors: Lucian Galescu, Choh Man Teng, James Allen, Ian Perera
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue Management, Dialogue State Tracking

The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell.
Identifying Domain Independent Update Intents in Task Based Dialogs
Authors: Prakhar Biyani, Cem Akkaya, Kostas Tsioutsiouliklis
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Multi-class Classification, Spoken Language Understanding, Vocal Bursts Type Prediction

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA{'}s fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI{'}s, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM{'}s to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content
Authors: Isuru Gunasekara, Isar Nejadgholi
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, text-classification, Text Classification, Word Embeddings

Language toxicity identification presents a gray area in the ethical debate surrounding freedom of speech and censorship. Today{'}s social media landscape is littered with unfiltered content that can be anywhere from slightly abusive to hate inducing. In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content. This content is typically colloquial and conversational in style. Its classification therefore requires huge amounts of annotated data due to its variability and inconsistency. We compare standard methods of text classification in this task. A conventional one-vs-rest SVM classifier with character and word level frequency-based representation of text reaches 0.9763 ROC AUC score. We demonstrated that leveraging more advanced technologies such as word embeddings, recurrent neural networks, attention mechanism, stacking of classifiers and semi-supervised training can improve the ROC AUC score of classification to 0.9862. We suggest that in order to choose the right model one has to consider the accuracy of models as well as inference complexity based on the application.
Aggression Detection on Social Media Text Using Deep Neural Networks
Authors: Vinay Singh, Aman Varshney, Syed Sarfaraz Akhtar, Deepanshu Vijay, Manish Shrivastava
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification

In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by \textbf{TRAC-1}in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: {``}Overtly Aggressive{''}, {``}Covertly Aggressive{''} and {``}Non-Aggressive{''}. We experimented with 6 classification models and our CNN model on a 10 K-fold cross-validation gave the best result with the prediction accuracy of 73.2{\%}.
Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying
Authors: Rachele Sprugnoli, Stefano Menini, Sara Tonelli, Filippo Oncini, Enrico Piras
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Although WhatsApp is used by teenagers as one major channel of cyberbullying, such interactions remain invisible due to the app privacy policies that do not allow ex-post data collection. Indeed, most of the information on these phenomena rely on surveys regarding self-reported data. In order to overcome this limitation, we describe in this paper the activities that led to the creation of a WhatsApp dataset to study cyberbullying among Italian students aged 12-13. We present not only the collected chats with annotations about user role and type of offense, but also the living lab created in a collaboration between researchers and schools to monitor and analyse cyberbullying. Finally, we discuss some open issues, dealing with ethical, operational and epistemic aspects.
Improving Moderation of Online Discussions via Interpretable Neural Models
Authors: Andrej {\v{S}}vec, Mat{\'u}{\v{s}} Pikuliak, Mari{\'a}n {\v{S}}imko, M{\'a}ria Bielikov{\'a}
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.
Aggressive language in an online hacking forum
Authors: Andrew Caines, Sergio Pastrana, Alice Hutchings, Paula Buttery
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abuse Detection, Abusive Language, domain classification

We probe the heterogeneity in levels of abusive language in different sections of the Internet, using an annotated corpus of Wikipedia page edit comments to train a binary classifier for abuse detection. Our test data come from the CrimeBB Corpus of hacking-related forum posts and we find that (a) forum interactions are rarely abusive, (b) the abusive language which does exist tends to be relatively mild compared to that found in the Wikipedia comments domain, and tends to involve aggressive posturing rather than hate speech or threats of violence. We observe that the purpose of conversations in online forums tend to be more constructive and informative than those in Wikipedia page edit comments which are geared more towards adversarial interactions, and that this may explain the lower levels of abuse found in our forum data than in Wikipedia comments. Further work remains to be done to compare these results with other inter-domain classification experiments, and to understand the impact of aggressive language in forum conversations.
The Effects of User Features on Twitter Hate Speech Detection
Authors: Elise Fehn Unsv{\aa}g, Bj{\"o}rn Gamb{\"a}ck
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Hate Speech Detection, Text Classification

The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.
Interpreting Neural Network Hate Speech Classifiers
Authors: Cindy Wang
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Hate Speech Detection, Text Classification

Deep neural networks have been applied to hate speech detection with apparent success, but they have limited practical applicability without transparency into the predictions they make. In this paper, we perform several experiments to visualize and understand a state-of-the-art neural network classifier for hate speech (Zhang et al., 2018). We adapt techniques from computer vision to visualize sensitive regions of the input stimuli and identify the features learned by individual neurons. We also introduce a method to discover the keywords that are most predictive of hate speech. Our analyses explain the aspects of neural networks that work well and point out areas for further improvement.
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks
Authors: Rijul Magu, Jiebo Luo
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abuse Detection, Abusive Language, Community Detection, Hate Speech Detection, Word Embeddings

While analysis of online explicit abusive language detection has lately seen an ever-increasing focus, implicit abuse detection remains a largely unexplored space. We carry out a study on a subcategory of implicit hate: euphemistic hate speech. We propose a method to assist in identifying unknown euphemisms (or code words) given a set of hateful tweets containing a known code word. Our approach leverages word embeddings and network analysis (through centrality measures and community detection) in a manner that can be generalized to identify euphemisms across contexts- not just hate speech.
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs
Authors: Sima Sharifirad, Borna Jafarpour, Stan Matwin
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abusive Language, BIG-bench Machine Learning, Classification, Dialogue Generation, General Classification, Knowledge Graphs, Machine Translation, Text Augmentation, text-classification, Text Classification, Text Generation, World Knowledge

Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikidata to improve sexist tweet classification by two methods (1) text augmentation and (2) text generation. In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity. In our text augmentation approach, the number of tweets remains the same but their words are augmented (concatenation) with words extracted from their ConceptNet relations and their description extracted from Wikidata. In our text augmentation approach, the number of tweets in each class remains the same but the range of each tweet increases. Our experiments show that our approach improves sexist tweet classification significantly in our entire machine learning models. Our approach can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.
Learning Representations for Detecting Abusive Language
Authors: Magnus Sahlgren, Tim Isbister, Fredrik Olsson
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abusive Language, Language Modeling, Language Modelling, Representation Learning, Text Classification, Transfer Learning, Word Embeddings

This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language. The approach is inspired by recent advances in transfer learning and word embeddings, and we learn representations from two different datasets containing various degrees of abusive language. We compare the learned representation with two standard approaches; one based on lexica, and one based on data-specific $n$-grams. Our experiments show that learned representations \textit{do} contain useful information that can be used to improve detection performance when training data is limited.
Datasets of Slovene and Croatian Moderated News Comments
Authors: Nikola Ljube{\v{s}}i{\'c}, Toma{\v{z}} Erjavec, Darja Fi{\v{s}}er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification

This paper presents two large newly constructed datasets of moderated news comments from two highly popular online news portals in the respective countries: the Slovene RTV MCC and the Croatian 24sata. The datasets are analyzed by performing manual annotation of the types of the content which have been deleted by moderators and by investigating deletion trends among users and threads. Next, initial experiments on automatically detecting the deleted content in the datasets are presented. Both datasets are published in encrypted form, to enable others to perform experiments on detecting content to be deleted without revealing potentially inappropriate content. Finally, the baseline classification models trained on the non-encrypted datasets are disseminated as well to enable real-world use.
Cross-Domain Detection of Abusive Language Online
Authors: Mladen Karan, Jan {\v{S}}najder
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abusive Language, Domain Adaptation, General Classification

We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one.
Did you offend me? Classification of Offensive Tweets in Hinglish Language
Authors: Puneet Mathur, Ramit Sawhney, Meghna Ayyar, Rajiv Shah
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abuse Detection, General Classification, text-classification, Text Classification, Transfer Learning

The use of code-switched languages (\textit{e.g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification.
Decipherment for Adversarial Offensive Language Detection
Authors: Zhelun Wu, Nishant Kambhatla, Anoop Sarkar
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Decipherment, Spelling Correction

Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter. In this paper, we model the disguised messages as if they are produced by encrypting the original message using an invented cipher. We apply automatic decipherment techniques to decode the disguised malicious text, which can be then filtered using rules or classifiers. We provide experimental results on three different datasets and show that decipherment is an effective tool for this task.
The Linguistic Ideologies of Deep Abusive Language Classification
Authors: Michael Castelle
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Abusive Language, Classification, General Classification

This paper brings together theories from sociolinguistics and linguistic anthropology to critically evaluate the so-called {``}language ideologies{''} {---} the set of beliefs and ways of speaking about language{---}in the practices of abusive language classification in modern machine learning-based NLP. This argument is made at both a conceptual and empirical level, as we review approaches to abusive language from different fields, and use two neural network methods to analyze three datasets developed for abusive language classification tasks (drawn from Wikipedia, Facebook, and StackOverflow). By evaluating and comparing these results, we argue for the importance of incorporating theories of pragmatics and metapragmatics into both the design of classification tasks as well as in ML architectures.
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning
Authors: Andrea Galassi, Marco Lippi, Paolo Torroni
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Argument Mining, Component Classification, Link Prediction, Relation Classification

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.
ArguminSci: A Tool for Analyzing Argumentation and Rhetorical Aspects in Scientific Writing
Authors: Anne Lauscher, Goran Glava{\v{s}}, Kai Eckert
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Argument Mining

Argumentation is arguably one of the central features of scientific language. We present \textit{ArguminSci}, an easy-to-use tool that analyzes argumentation and other rhetorical aspects of scientific writing, which we collectively dub \textit{scitorics}. The main aspect we focus on is the fine-grained argumentative analysis of scientific text through identification of argument components. The functionality of \textit{ArguminSci} is accessible via three interfaces: as a command line tool, via a RESTful application programming interface, and as a web application.
Evidence Type Classification in Randomized Controlled Trials
Authors: Tobias Mayer, Elena Cabrio, Serena Villata
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Argument Mining, Classification, Decision Making, General Classification, Vocal Bursts Type Prediction

Randomized Controlled Trials (RCT) are a common type of experimental studies in the medical domain for evidence-based decision making. The ability to automatically extract the \textit{arguments} proposed therein can be of valuable support for clinicians and practitioners in their daily evidence-based decision making activities. Given the peculiarity of the medical domain and the required level of detail, standard approaches to argument component detection in \textit{argument(ation) mining} are not fine-grained enough to support such activities. In this paper, we introduce a new sub-task of the argument component identification task: \textit{evidence type classification}. To address it, we propose a supervised approach and we test it on a set of RCT abstracts on different medical topics.
An Argument-Annotated Corpus of Scientific Publications
Authors: Anne Lauscher, Goran Glava{\v{s}}, Simone Paolo Ponzetto
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Argument Mining

Argumentation is an essential feature of scientific language. We present an annotation study resulting in a corpus of scientific publications annotated with argumentative components and relations. The argumentative annotations have been added to the existing Dr. Inventor Corpus, already annotated for four other rhetorical aspects. We analyze the annotated argumentative structures and investigate the relations between argumentation and other rhetorical aspects of scientific writing, such as discourse roles and citation contexts.