SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection

2020-12-01 · SEMEVAL 2020 · Daryna Dementieva, Igor Markov, Alexander Panchenko

This paper presents a solution for the Span Identification (SI) task in the {``}Detection of Propaganda Techniques in News Articles{''} competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.

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Articles Data AugmentationPropaganda detectionText Augmentation

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Multi-Head Attention 설명 없음

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

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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.

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SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations

2022-07-01 · SemEval (NAACL) 2022 7 · Mikhail Kuimov, Daryna Dementieva, Alexander Panchenko

This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we’ve considered sy…

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SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection

2021-08-01 · SEMEVAL 2021 · David Dale, Igor Markov, Varvara Logacheva, Olga Kozlova 외

This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a bi…

Sentence Toxic Spans Detection

IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts

2024-04-06 · Udvas Basak, Rajarshi Dutta, Shivam Pandey, Ashutosh Modi

This paper describes our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness. The challenge is focused on automatically detecting the degree of relatedness between pairs of sentences for 14 languag…

Contrastive Learning Word Embeddings

SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task

2021-08-01 · SEMEVAL 2021 · Anton Razzhigaev, Nikolay Arefyev, Alexander Panchenko

In this paper, we present a system for the solution of the cross-lingual and multilingual word-in-context disambiguation task. Task organizers provided monolingual data in several languages, but no cross-lingual training…

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Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

2020-12-01 · SEMEVAL 2020 · Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi

In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity me…

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