pwc-archive/papers-with-abstracts huggingface dataset:
Diffeomorphic Temporal Alignment Nets
Authors: Ron Shapira Weber, Matan Eyal, Nicki Skafte, Oren Shriki, Oren Freifeld
Date: 2019-12-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: ECG Classification, Electrocardiography (ECG), Time Series, Time Series Alignment, Time Series Analysis, Time Series Averaging

Time-series analysis is confounded by nonlinear time warping of the data. Traditional methods for joint alignment do not generalize: after aligning a given signal ensemble, they lack a mechanism, that does not require solving a new optimization problem, to align previously-unseen signals. In the multi-class case, they must also first classify the test data before aligning it. Here we propose the Diffeomorphic Temporal alignment Net (DTAN), a learning-based method for time-series joint alignment. Via flexible temporal transformer layers, DTAN learns and applies an input-dependent nonlinear time warping to its input signal. Once learned, DTAN easily aligns previously-unseen signals by its inexpensive forward pass. In a single-class case, the method is unsupervised: the ground-truth alignments are unknown. In the multi-class case, it is semi-supervised in the sense that class labels (but not the ground-truth alignments) are used during learning; in test time, however, the class labels are unknown. As we show, DTAN not only outperforms existing joint-alignment methods in aligning training data but also generalizes well to test data. Our code is available at https://github.com/BGU-CS-VIL/dtan.
A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models
Authors: Olaf Wallaart; Flavius Frasincar
Date: 2019-03-04 00:00:00
Proceeding: ESWC 2019
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

This work focuses on sentence-level aspect-based sentiment
analysis for restaurant reviews. A two-stage sentiment analysis algorithm
is proposed. In this method, first a lexicalized domain ontology is used to
predict the sentiment and as a back-up algorithm a neural network with
a rotatory attention mechanism (LCR-Rot) is utilized. Furthermore, two
features are added to the backup algorithm. The first extension changes
the order in which the rotatory attention mechanism operates (LCRRot-inv). The second extension runs over the rotatory attention mechanism for multiple iterations (LCR-Rot-hop). Using the SemEval-2015
and SemEval-2016 data, we conclude that the two-stage method outperforms the baseline methods, albeit with a small percentage. Moreover,
we find that the method where we iterate multiple times over a rotatory
attention mechanism has the best performance.
Feature Enhancement with Deep Feature Losses for Speaker Verification
Authors: Saurabh Kataria, Phani Sankar Nidadavolu, Jesús Villalba, Nanxin Chen, Paola García, Najim Dehak
Date: 2019-10-25 00:00:00
Tasks: Denoising, Speaker Verification, Speech Enhancement

Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enhancement and propose a feature-domain supervised denoising based solution. We propose to use Deep Feature Loss which optimizes the enhancement network in the hidden activation space of a pre-trained auxiliary speaker embedding network. We experimentally verify the approach on simulated and real data. A simulated testing setup is created using various noise types at different SNR levels. For evaluation on real data, we choose BabyTrain corpus which consists of children recordings in uncontrolled environments. We observe consistent gains in every condition over the state-of-the-art augmented Factorized-TDNN x-vector system. On BabyTrain corpus, we observe relative gains of 10.38% and 12.40% in minDCF and EER respectively.
Detecting context abusiveness using hierarchical deep learning
Authors: Ju-Hyoung Lee, Jun-U Park, Jeong-Won Cha, Yo-Sub Han
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Deep Learning

Abusive text is a serious problem in social media and causes many issues among users as the number of users and the content volume increase. There are several attempts for detecting or preventing abusive text effectively. One simple yet effective approach is to use an abusive lexicon and determine the existence of an abusive word in text. This approach works well even when an abusive word is obfuscated. On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words. Especially, it is hard to identify sarcasm or offensiveness in context without any abusive words. We tackle this problem using an ensemble deep learning model. Our model consists of two parts of extracting local features and global features, which are crucial for identifying implicit abusiveness in context level. We evaluate our model using three benchmark data. Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words. Furthermore, we combine our model and an abusive lexicon method. The experimental results show that our model has at least 4{\%} better performance compared with the previous approaches for identifying text abusiveness in case of with/without abusive words.
How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm
Authors: Nayeema Nasrin, Kim-Kwang Raymond Choo, Myung Ko, Anthony Rios
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4{\%} F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by {\textasciitilde}9{\%} F1.
Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential
Authors: Anna Rogers, Olga Kovaleva, Anna Rumshisky
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship. We investigate the possibility of their automatic detection and their potential for predicting real-world protest events, on historical data of Bolotnaya protests in Russia (2011-2013). We find that political calls to action can be annotated and detected with relatively high accuracy, and that in our sample their volume has a moderate positive correlation with rally attendance.
Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
Authors: ChaeHun Park, Wonsuk Yang, Jong Park
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Diversity, Retrieval

Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition
Authors: Wei-Fan Chen, Khalid Al Khatib, Matthias Hagen, Henning Wachsmuth, Benno Stein
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Abusive Language

Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic. Since an automatic detection of abusive language can support human moderators of online discussion platforms, detecting abusiveness has recently received increased attention. However, the existing approaches simply train one classifier for the whole variety of abusiveness. In contrast, our approach is to distinguish explicitly abusive cases from the more {``}shadowed{''} ones. By dynamically extending a lexicon of abusive terms (e.g., including new obfuscations of abusive terms), our approach can support a moderator with explicit unraveled explanations for why something was flagged as abusive: due to known explicitly abusive terms, due to newly detected (obfuscated) terms, or due to shadowed cases.
CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection
Authors: Wenjun Hou, Ying Chen
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Propaganda detection, Sentence

The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In order to better utilize the document information, we construct context-dependent input pairs (sentence-title pair and sentence- context pair) to fine-tune the pretrained BERT, and we also use the undersampling method to tackle the problem of imbalanced data.
Fine-Grained Propaganda Detection with Fine-Tuned BERT
Authors: Shehel Yoosuf, Yin Yang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, Classification, General Classification, Language Modeling, Language Modelling, Propaganda detection

This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF{'}19 workshop. The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. The main idea of our solution is to perform word-level classification using fine-tuned BERT, a popular pre-trained language model. Besides presenting the model and its evaluation results, we also investigate the attention heads in the model, which provide insights into what the model learns, as well as aspects for potential improvements.
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification
Authors: Norman Mapes, Anna White, Radhika Medury, Sumeet Dua
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, Binary Classification, Language Modelling, Propaganda detection, Sentence

On the NLP4IF 2019 sentence level propaganda classification task, we used a BERT language model that was pre-trained on Wikipedia and BookCorpus as team ltuorp ranking {\#}1 of 26. It uses deep learning in the form of an attention transformer. We substituted the final layer of the neural network to a linear real valued output neuron from a layer of softmaxes. The backpropagation trained the entire neural network and not just the last layer. Training took 3 epochs and on our computation resources this took approximately one day. The pre-trained model consisted of uncased words and there were 12-layers, 768-hidden neurons with 12-heads for a total of 110 million parameters. The articles used in the training data promote divisive language similar to state-actor-funded influence operations on social media. Twitter shows state-sponsored examples designed to maximize division occurring across political lines, ranging from {``}Obama calls me a clinger, Hillary calls me deplorable, ... and Trump calls me an American{''} oriented to the political right, to Russian propaganda featuring {``}Black Lives Matter{''} material with suggestions of institutional racism in US police forces oriented to the political left. We hope that raising awareness through our work will reduce the polarizing dialogue for the betterment of nations.
On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings
Authors: Andr{\'e} Ferreira Cruz, Gil Rocha, Henrique Lopes Cardoso
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Propaganda detection, Sentence, Word Embeddings

Bias is ubiquitous in most online sources of natural language, from news media to social networks. Given the steady shift in news consumption behavior from traditional outlets to online sources, the automatic detection of propaganda, in which information is shaped to purposefully foster a predetermined agenda, is an increasingly crucial task. To this goal, we explore the task of sentence-level propaganda detection, and experiment with both handcrafted features and learned dense semantic representations. We also experiment with random undersampling of the majority class (non-propaganda) to curb the influence of class distribution on the system{'}s performance, leading to marked improvements on the minority class (propaganda). Our best performing system uses pre-trained ELMo word embeddings, followed by a bidirectional LSTM and an attention layer. We have submitted a 5-model ensemble of our best performing system to the NLP4IF shared task on sentence-level propaganda detection (team LIACC), achieving rank 10 among 25 participants, with 59.5 F1-score.
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models
Authors: Hani Al-Omari, Malak Abdullah, Ola AlTiti, Samira Shaikh
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Logical Fallacies, Propaganda detection, Sentence

The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining {``}fake news{''} is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda. Digital content production technologies with logical fallacies and emotional language can be used as propaganda techniques to gain more readers or mislead the audience. Recently, several researchers have proposed deep learning (DL) models to address this issue. This research paper provides an ensemble deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda. The proposed model has been applied on the dataset provided by the challenge NLP4IF 2019, Task 1 Sentence Level Classification (SLC) and it shows a significant performance over the baseline model.
Detection of Propaganda Using Logistic Regression
Authors: Jinfen Li, Zhihao Ye, Lu Xiao
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Logical Fallacies, regression, Sentence

Various propaganda techniques are used to manipulate peoples perspectives in order to foster a predetermined agenda such as by the use of logical fallacies or appealing to the emotions of the audience. In this paper, we develop a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. We utilize features like TF-IDF, BERT vector, sentence length, readability grade level, emotion feature, LIWC feature and emphatic content feature to help us differentiate these two categories. The linguistic and semantic features combination results in 66.16{\%} of F1 score, which outperforms the baseline hugely.
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
Authors: Harish Tayyar Madabushi, Elena Kochkina, Michael Castelle
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, Data Augmentation, General Classification, Propaganda detection, Sentence, Sentence Classification, Sentiment Analysis, text-classification, Text Classification

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second highest score on sentence-level propaganda classification.
Pretrained Ensemble Learning for Fine-Grained Propaganda Detection
Authors: Ali Fadel, Ibraheem Tuffaha, Mahmoud Al-Ayyoub
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Ensemble Learning, Propaganda detection, Sentence

In this paper, we describe our team{'}s effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference. Our top performing system results come from applying ensemble average on three pretrained models to make their predictions. The first two models use the uncased and cased versions of Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) while the third model uses Universal Sentence Encoder (USE) (Cer et al. 2018). Out of 26 participating teams, our system is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3870 F1-score on the testing dataset.
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning
Authors: Kartik Aggarwal, Anubhav Sadana
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, Propaganda detection, Sentence, Transfer Learning

In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. Given a sentence from a news article, the task is to detect whether the sentence contains a propagandistic agenda or not. The main contribution of our work is to evaluate the effectiveness of various transfer learning approaches like ELMo, BERT, and RoBERTa for propaganda detection. We show the use of Document Embeddings on the top of Stacked Embeddings combined with LSTM for identification of propagandistic context in the sentence. We further provide analysis of these models to show the effect of oversampling on the provided dataset. In the final test-set evaluation, our system ranked 21st with F1-score of 0.43 in the SLC Task.
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model
Authors: George-Alex Vlad, ru, Mircea-Adrian Tanase, Cristian Onose, Dumitru-Clementin Cercel
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, Emotion Classification, General Classification, Propaganda detection, Sentence, Transfer Learning

In recent years, the need for communication increased in online social media. Propaganda is a mechanism which was used throughout history to influence public opinion and it is gaining a new dimension with the rising interest of online social media. This paper presents our submission to NLP4IF-2019 Shared Task SLC: Sentence-level Propaganda Detection in news articles. The challenge of this task is to build a robust binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Our model relies on a unified neural network, which consists of several deep leaning modules, namely BERT, BiLSTM and Capsule, to solve the sentencelevel propaganda classification problem. In addition, we take a pre-training approach on a somewhat similar task (i.e., emotion classification) improving results against the cold-start model. Among the 26 participant teams in the NLP4IF-2019 Task SLC, our solution ranked 12th with an F1-score 0.5868 on the official test data. Our proposed solution indicates promising results since our system significantly exceeds the baseline approach of the organizers by 0.1521 and is slightly lower than the winning system by 0.0454.
Synthetic Propaganda Embeddings To Train A Linear Projection
Authors: Adam Ek, Mehdi Ghanimifard
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Language Modeling, Language Modelling, Propaganda detection, Sentence

This paper presents a method of detecting fine-grained categories of propaganda in text. Given a sentence, our method aims to identify a span of words and predict the type of propaganda used. To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without fine-tuning the large parameters of the base model. We show that by generating synthetic embeddings we can train a linear function with ReLU activation to extract useful labeled embeddings from an embedding space generated by a general-purpose language model. We also introduce an inference technique to detect continuous spans in sequences of propaganda tokens in sentences. A result of the ensemble model is submitted to the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. In this paper, we provide additional analysis regarding our method of detecting spans of propaganda with synthetically generated representations.
Extracting Complex Relations from Banking Documents
Authors: Berke Oral, Erdem Emekligil, Se{\c{c}}il Arslan, G{\"u}l{\c{s}}en Eryi{\u{g}}it
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Relation, Relation Extraction

In order to automate banking processes (e.g. payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners. Banking orders may be considered as complex documents since they contain quite complex relations compared to traditional datasets used in relation extraction research. In this paper, we present our method to extract intersentential, nested and complex relations from banking orders, and introduce a relation extraction method based on maximal clique factorization technique. We demonstrate 11{\%} error reduction over previous methods.