Articles

AbstractThis paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plain text, …...

AbstractThe paper presents the solution of team ”Inno” to a SEMEVAL 2020 task 11 ”Detection of propaganda techniques in news articles”. The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure …...

AbstractThis paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are …...

AbstractWe describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, …...

AbstractThis paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media. Given a sentence, we are asked to find out the most important words as the suggestion for automated design. We leverage the unsupervised pre-training …...

AbstractThis paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans …...

AbstractThis paper describes Galileo’s performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by …...

AbstractWe present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. …...

AbstractThis paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purpose of TC task was to identify an applied propaganda technique given propaganda text fragment. The goal of SI task was to find specific text …...

AbstractWe present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a …...

AbstractWe propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior …...

AbstractIn this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design …...

AbstractThis paper reports the zyy1510 team’s work in the International Workshop on Semantic Evaluation (SemEval-2020) shared task on Sentiment analysis for Code-Mixed (Hindi-English, English-Spanish) Social Media Text. The purpose of this task is to determine the polarity of the text, dividing it into one of the three labels positive, negative …...

AbstractIn this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed …...

AbstractSentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult …...

AbstractIn social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained …...

AbstractCommonly occurring in settings such as social media platforms, code-mixed content makes the task of identifying sentiment notably more challenging and complex due to the lack of structure and noise present in the data. SemEval-2020 Task 9, SentiMix, was organized with the purpose of detecting the sentiment of a given …...

AbstractMixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of society and political trends. In this paper, we propose an ensemble of pesudo-label based Bert model and TFIDF based SGDClassifier …...

AbstractWe explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During …...

AbstractCode-mixing is an interesting phenomenon where the speaker switches between two or more languages in the same text. In this paper, we describe an unconventional approach to tackling the SentiMix Hindi-English challenge (UID: aditya_malte). Instead of directly fine-tuning large contemporary Transformer models, we train our own domain-specific embeddings and make …...