newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification

2020-07-21 · SEMEVAL 2020 · Paramansh Singh, Siraj Sandhu, Subham Kumar, Ashutosh Modi

This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.

📄 PDF Abstract BibTeX arXiv:2007.10827

Code (1)

paramansh/propaganda_detection 공식 구현 pytorch

Tasks

Articles Classification General ClassificationLanguage ModelingLanguage Modellingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

WordPiece 설명 없음

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…

Attention 설명 없음

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Residual Connection 설명 없음

JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction

2021-08-01 · SEMEVAL 2021 · Chaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within …

Lexical Complexity Prediction Word Embeddings

YNU\_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification

2019-06-01 · SEMEVAL 2019 6 · Yunxia Ding, Xiaobing Zhou, Xue-jie Zhang

In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed representations from sentences. …

General Classification Suggestion mining

MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis

2019-06-01 · SEMEVAL 2019 6 · Yoonhyung Lee, Yanghoon Kim, Kyomin Jung

This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting th…

Sentiment Analysis

NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning

2019-06-01 · SEMEVAL 2019 6 · Rol Potamias, os Alex, ros, Georgios Siolas

Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related dat…

Sentiment Analysis

NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension

2021-05-25 · SEMEVAL 2021 · Zhixiang Chen, Yikun Lei, Pai Liu, Guibing Guo

SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware m…

Machine Reading Comprehension Reading Comprehension