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NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier

2019-04-10 · SEMEVAL 2019 6 · Alessandro Seganti, Helena Sobol, Iryna Orlova, Hannam Kim, Jakub Staniszewski, Tymoteusz Krumholc, Krystian Koziel

The paper presents a system developed for the SemEval-2019 competition Task 5 hat-Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI's GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.

📄 PDF Abstract BibTeX arXiv:1904.05152

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

Cosine Annealing Cosine Annealing is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before…

Weight Decay 설명 없음

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Adam 설명 없음

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

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Attention 설명 없음

SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection

2021-08-01 · SEMEVAL 2021 · Micha{\l} Sat{\l}awa, Katarzyna Zam{\l}y{\'n}ska, Jaros{\l}aw Piersa, Joanna Kolis 외

This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified an…

Classification Toxic Spans Detection

TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

2019-05-29 · SEMEVAL 2019 6 · Piotr Niewinski, Aleksander Wawer, Maria Pszona, Maria Janicka

The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/ad…

Community Question Answering Fact Checking Question Answering Sentence

Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles

2022-07-01 · SemEval (NAACL) 2022 7 · Tomasz Dryjański, Monika Zaleska, Bartek Kuźma, Artur Błażejewski 외

In this work we present an overview of our winning system for the R2VQ - Competence-based Multimodal Question Answering task, with the final exact match score of 92.53%.The task is structured as question-answer pairs, qu…

Question Answering Response Generation

NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets

2018-06-01 · SEMEVAL 2018 6 · Harsh Rangwani, Devang Kulshreshtha, Anil Kumar Singh

This paper describes our participation in SemEval 2018 Task 3 on Irony Detection in Tweets. We combine linguistic features with pre-trained activations of a neural network. The CNN is trained on the emoji prediction task…

Classification General Classification Sarcasm Detection

CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extraction

2016-06-01 · SEMEVAL 2016 6 · Charlotte Hansart, Damien De Meyere, Patrick Watrin, Andr{\'e} Bittar 외

Part-Of-Speech Tagging Temporal Information Extraction