pwc-archive/papers-with-abstracts huggingface dataset:
A Corpus of the Sorani Kurdish Folkloric Lyrics
Authors: Sina Ahmadi, Hossein Hassani, Kamaladdin Abedi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Attribute

Kurdish poetry and prose narratives were historically transmitted orally and less in a written form. Being an essential medium of oral narration and literature, Kurdish lyrics have had a unique attribute in becoming a vital resource for different types of studies, including Digital Humanities, Computational Folkloristics and Computational Linguistics. As an initial study of its kind for the Kurdish language, this paper presents our efforts in transcribing and collecting Kurdish folk lyrics as a corpus that covers various Kurdish musical genres, in particular Beyt, Gorani, Bend, and Heyran. We believe that this corpus contributes to Kurdish language processing in several ways, such as compensation for the lack of a long history of written text by incorporating oral literature, presenting an unexplored realm in Kurdish language processing, and assisting the initiation of Kurdish computational folkloristics. Our corpus contains 49,582 tokens in the Sorani dialect of Kurdish. The corpus is publicly available in the Text Encoding Initiative (TEI) format for non-commercial use.
Improving the Language Model for Low-Resource ASR with Online Text Corpora
Authors: Nils Hjortnaes, Timofey Arkhangelskiy, Niko Partanen, Michael Rie{\ss}ler, Francis Tyers
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Transfer Learning

In this paper, we expand on previous work on automatic speech recognition in a low-resource scenario typical of data collected by field linguists. We train DeepSpeech models on 35 hours of dialectal Komi speech recordings and correct the output using language models constructed from various sources. Previous experiments showed that transfer learning using DeepSpeech can improve the accuracy of a speech recognizer for Komi, though the error rate remained very high. In this paper we present further experiments with language models created using KenLM from text materials available online. These are constructed from two corpora, one containing literary texts, one for social media content, and another combining the two. We then trained the model using each language model to explore the impact of the language model data source on the speech recognition model. Our results show significant improvements of over 25{\%} in character error rate and nearly 20{\%} in word error rate. This offers important methodological insight into how ASR results can be improved under low-resource conditions: transfer learning can be used to compensate the lack of training data in the target language, and online texts are a very useful resource when developing language models in this context.
``A Passage to India'': Pre-trained Word Embeddings for Indian Languages
Authors: Saurav Kumar, Saunack Kumar, Diptesh Kanojia, Pushpak Bhattacharyya
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Information Retrieval, Machine Translation, NER, Question Answering, Retrieval, Translation, Word Embeddings, Word Sense Disambiguation

Dense word vectors or {`}word embeddings{'} which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT), Question Answering (QA), Word Sense Disambiguation (WSD), and Information Retrieval (IR). In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages, \textit{viz.}, Assamese, Bengali, Gujarati, Hindi, Kannada, Konkani, Malayalam, Marathi, Nepali, Odiya, Punjabi, Sanskrit, Tamil, and Telugu in a single repository. Relatively newer approaches that emphasize catering to context (BERT, ELMo, \textit{etc.}) have shown significant improvements, but require a large amount of resources to generate usable models. We release pre-trained embeddings generated using both contextual and non-contextual approaches. We also use MUSE and XLM to train cross-lingual embeddings for all pairs of the aforementioned languages. To show the efficacy of our embeddings, we evaluate our embedding models on XPOS, UPOS and NER tasks for all these languages. We release a total of 436 models using 8 different approaches. We hope they are useful for the resource-constrained Indian language NLP. The title of this paper refers to the famous novel {``}A Passage to India{''} by E.M. Forster, published initially in 1924.
A Counselling Corpus in Cantonese
Authors: John Lee, Tianyuan Cai, Wenxiu Xie, Lam Xing
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Chatbot

Virtual agents are increasingly used for delivering health information in general, and mental health assistance in particular. This paper presents a corpus designed for training a virtual counsellor in Cantonese, a variety of Chinese. The corpus consists of a domain-independent subcorpus that supports small talk for rapport building with users, and a domain-specific subcorpus that provides material for a particular area of counselling. The former consists of ELIZA style responses, chitchat expressions, and a dataset of general dialog, all of which are reusable across counselling domains. The latter consists of example user inputs and appropriate chatbot replies relevant to the specific domain. In a case study, we created a chatbot with a domain-specific subcorpus that addressed 25 issues in test anxiety, with 436 inputs solicited from native speakers of Cantonese and 150 chatbot replies harvested from mental health websites. Preliminary evaluations show that Word Mover{'}s Distance achieved 56{\%} accuracy in identifying the issue in user input, outperforming a number of baselines.
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree
Authors: Vishwa Gupta, Gilles Boulianne
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling, Phoneme Recognition, speech-recognition, Speech Recognition

Cree is one of the most spoken Indigenous languages in Canada. From a speech recognition perspective, it is a low-resource language, since very little data is available for either acoustic or language modeling. This has prevented development of speech technology that could help revitalize the language. We describe our experiments with available Cree data to improve automatic transcription both in speaker- independent and dependent scenarios. While it was difficult to get low speaker-independent word error rates with only six speakers, we were able to get low word and phoneme error rates in the speaker-dependent scenario. We compare our phoneme recognition with two state-of-the-art open-source phoneme recognition toolkits, which use end-to-end training and sequence-to-sequence modeling. Our phoneme error rate (8.7{\%}) is significantly lower than that achieved by the best of these systems (15.1{\%}). With these systems and varying amounts of transcribed and text data, we show that pre-training on other languages is important for speaker-independent recognition, and even small amounts of additional text-only documents are useful. These results can guide practical language documentation work, when deciding how much transcribed and text data is needed to achieve useful phoneme accuracies.
Turkish Emotion Voice Database (TurEV-DB)
Authors: Salih Firat Canpolat, Zuhal Ormano{\u{g}}lu, Deniz Zeyrek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning

We introduce the Turkish Emotion-Voice Database (TurEV-DB) which involves a corpus of over 1700 tokens based on 82 words uttered by human subjects in four different emotions (\textit{angry, calm, happy, sad}). Three machine learning experiments are run on the corpus data to classify the emotions using a convolutional neural network (CNN) model and a support vector machine (SVM) model. We report the performance of the machine learning models, and for evaluation, compare machine learning results with the judgements of humans.
Active Defense Against Social Engineering: The Case for Human Language Technology
Authors: Adam Dalton, Ehsan Aghaei, Ehab Al-Shaer, Archna Bhatia, Esteban Castillo, Zhuo Cheng, Sreekar Dhaduvai, Qi Duan, Bryanna Hebenstreit, Md Mazharul Islam, Younes Karimi, Amir Masoumzadeh, Brodie Mather, Sashank Santhanam, Samira Shaikh, Alan Zemel, Tomek Strzalkowski, Bonnie J. Dorr
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Attribute, Dialogue Generation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker{'}s time and resources.
Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks
Authors: Jin-Hwa Kim, Yoon Jo Kim, Mitra Behzadi, Ian G. Harris
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification

We present an automated approach to analyze the text of an online conversation and determine whether one of the participants is a cyberpredator who is preying on another participant. The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation. Each stage uses a Recurrent Neural Network (RNN) to perform the classification task.
A Privacy Preserving Data Publishing Middleware for Unstructured, Textual Social Media Data
Authors: Prasadi Abeywardana, Uthayasanker Thayasivam
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Privacy Preserving

Privacy is going to be an integral part of data science and analytics in the coming years. The next hype of data experimentation is going to be heavily dependent on privacy preserving techniques mainly as it{'}s going to be a legal responsibility rather than a mere social responsibility. Privacy preservation becomes more challenging specially in the context of unstructured data. Social networks have become predominantly popular over the past couple of decades and they are creating a huge data lake at a high velocity. Social media profiles contain a wealth of personal and sensitive information, creating enormous opportunities for third parties to analyze them with different algorithms, draw conclusions and use in disinformation campaigns and micro targeting based dark advertising. This study provides a mitigation mechanism for disinformation campaigns that are done based on the insights extracted from personal/sensitive data analysis. Specifically, this research is aimed at building a privacy preserving data publishing middleware for unstructured social media data without compromising the true analytical value of those data. A novel way is proposed to apply traditional structured privacy preserving techniques on unstructured data. Creating a comprehensive twitter corpus annotated with privacy attributes is another objective of this research, especially because the research community is lacking one.
Information Space Dashboard
Authors: Theresa Krumbiegel, Albert Pritzkau, Hans-Christian Schmitz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Event Extraction, Sentiment Analysis, text-classification, Text Classification

The information space, where information is generated, stored, exchanged and discussed, is not idyllic but a space where campaigns of disinformation and destabilization are conducted. Such campaigns are subsumed under the terms {``}hybrid warfare{''} and {``}information warfare{''} (Woolley and Howard, 2017). In order to enable awareness of them, we propose an information state dashboard comprising various components/apps for data collection, analysis and visualization. The aim of the dashboard is to support an analyst in generating a common operational picture of the information space, link it with an operational picture of the physical space and, thus, contribute to overarching situational awareness. The dashboard is work in progress. However, a first prototype with components for exploiting elementary language statistics, keyword and metadata analysis, text classification and network analysis has been implemented. Further components, in particular, for event extraction and sentiment analysis are under development. As a demonstration case, we briefly discuss the analysis of historical data regarding violent anti-migrant protests and respective counter-protests that took place in Chemnitz in 2018.
Is this hotel review truthful or deceptive? A platform for disinformation detection through computational stylometry
Authors: Antonio Pascucci, Raffaele Manna, Ciro Caterino, Vincenzo Masucci, Johanna Monti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we present a web service platform for disinformation detection in hotel reviews written in English. The platform relies on a hybrid approach of computational stylometry techniques, machine learning and linguistic rules written using COGITO, Expert System Corp.{'}s semantic intelligence software thanks to which it is possible to analyze texts and extract all their characteristics. We carried out a research experiment on the Deceptive Opinion Spam corpus, a balanced corpus composed of 1,600 hotel reviews of 20 Chicago hotels split into four datasets: positive truthful, negative truthful, positive deceptive and negative deceptive reviews. We investigated four different classifiers and we detected that Simple Logistic is the most performing algorithm for this type of classification.
Corpus Development for Studying Online Disinformation Campaign: A Narrative + Stance Approach
Authors: Mack Blackburn, Ning Yu, John Berrie, Brian Gordon, David Longfellow, William Tirrell, Mark Williams
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Disinformation on social media is impacting our personal life and society. The outbreak of the new coronavirus is the most recent example for which a wealth of disinformation provoked fear, hate, and even social panic. While there are emerging interests in studying how disinformation campaigns form, spread, and influence target audiences, developing disinformation campaign corpora is challenging given the high volume, fast evolution, and wide variation of messages associated with each campaign. Disinformation cannot always be captured by simple factchecking, which makes it even more challenging to validate and create ground truth. This paper presents our approach to develop a corpus for studying disinformation campaigns targeting the White Helmets of Syria. We bypass directly classifying a piece of information as disinformation or not. Instead, we label the narrative and stance of tweets and YouTube comments about White Helmets. Narratives is defined as a recurring statement that is used to express a point of view. Stance is a high-level point of view on a topic. We demonstrate that narrative and stance together can provide a dynamic method for real world users, e.g., intelligence analysts, to quickly identify and counter disinformation campaigns based on their knowledge at the time.
Email Threat Detection Using Distinct Neural Network Approaches
Authors: Esteban Castillo, Sreekar Dhaduvai, Peng Liu, Kartik-Singh Thakur, Adam Dalton, Tomek Strzalkowski
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning

This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.
Evaluating Aggression Identification in Social Media
Authors: Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, Marcos Zampieri
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Aggression Identification

In this paper, we present the report and findings of the Shared Task on Aggression and Gendered Aggression Identification organised as part of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC - 2) at LREC 2020. The task consisted of two sub-tasks - aggression identification (sub-task A) and gendered identification (sub-task B) - in three languages - Bangla, Hindi and English. For this task, the participants were provided with a dataset of approximately 5,000 instances from YouTube comments in each language. For testing, approximately 1,000 instances were provided in each language for each sub-task. A total of 70 teams registered to participate in the task and 19 teams submitted their test runs. The best system obtained a weighted F-score of approximately 0.80 in sub-task A for all the three languages. While approximately 0.87 in sub-task B for all the three languages.
TOCP: A Dataset for Chinese Profanity Processing
Authors: Hsu Yang, Chuan-Jie Lin
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper introduced TOCP, a larger dataset of Chinese profanity. This dataset contains natural sentences collected from social media sites, the profane expressions appearing in the sentences, and their rephrasing suggestions which preserve their meanings in a less offensive way. We proposed several baseline systems using neural network models to test this benchmark. We trained embedding models on a profanity-related dataset and proposed several profanity-related features. Our baseline systems achieved an F1-score of 86.37{\%} in profanity detection and an accuracy of 77.32{\%} in profanity rephrasing.
A Multi-Dimensional View of Aggression when voicing Opinion
Authors: Arjit Srivastava, Avijit Vajpayee, Syed Sarfaraz Akhtar, Naman jain, Vinay Singh, Manish Shrivastava
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The advent of social media has immensely proliferated the amount of opinions and arguments voiced on the internet. These virtual debates often present cases of aggression. While research has been focused largely on analyzing aggression and stance in isolation from each other, this work is the first attempt to gain an extensive and fine-grained understanding of patterns of aggression and figurative language use when voicing opinion. We present a Hindi-English code-mixed dataset of opinion on the politico-social issue of {`}2016 India banknote demonetisation{`} and annotate it across multiple dimensions such as aggression, hate speech, emotion arousal and figurative language usage (such as sarcasm/irony, metaphors/similes, puns/word-play).
Aggression Identification in Social Media: a Transfer Learning Based Approach
Authors: Rami, Faneva risoa, Josiane Mothe
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Aggression Identification, Language Modeling, Language Modelling, Transfer Learning

The way people communicate have changed in many ways with the outbreak of social media. One of the aspects of social media is the ability for their information producers to hide, fully or partially, their identity during a discussion; leading to cyber-aggression and interpersonal aggression. Automatically monitoring user-generated content in order to help moderating it is thus a very hot topic. In this paper, we propose to use the transformer based language model BERT (Bidirectional Encoder Representation from Transformer) (Devlin et al., 2019) to identify aggressive content. Our model is also used to predict the level of aggressiveness. The evaluation part of this paper is based on the dataset provided by the TRAC shared task (Kumar et al., 2018a). When compared to the other participants of this shared task, our model achieved the third best performance according to the weighted F1 measure on both Facebook and Twitter collections.
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text
Authors: Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, Paul Buitelaar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abuse Detection, Meme Classification

A meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Hate speech, offensive content and aggression content detection have been extensively explored in a single modality such as text or image. However, combining two modalities to detect offensive content is still a developing area. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. Therefore, it is necessary to combine both modalities to identify whether a given meme is offensive or not. Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U.S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset. We subsequently developed a classifier for this task using the MultiOFF dataset. We use an early fusion technique to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness. Our results show improvements in terms of Precision, Recall, and F-Score. The code and dataset for this paper is published in \textit{ \url{https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text} }
A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data
Authors: Priya Rani, Shardul Suryawanshi, Koustava Goswami, Bharathi Raja Chakravarthi, Theodorus Fransen, John Philip McCrae
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Hate Speech Detection

Hate speech detection in social media communication has become one of the primary concerns to avoid conflicts and curb undesired activities. In an environment where multilingual speakers switch among multiple languages, hate speech detection becomes a challenging task using methods that are designed for monolingual corpora. In our work, we attempt to analyze, detect and provide a comparative study of hate speech in a code-mixed social media text. We also provide a Hindi-English code-mixed data set consisting of Facebook and Twitter posts and comments. Our experiments show that deep learning models trained on this code-mixed corpus perform better.