5 citations · 5 across the 3 of their papers we have counts for
5 papers
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev +2
While there has been substantial progress in developing systems to automate fact-checking, they still lack credibility in the eyes of the users. Thus, an interesting approach has e…
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks
Anton Chernyavskiy, Dmitry Ilvovsky, Pavel Kalinin +1
The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP). Here, we expl…
WhatTheWikiFact: Fact-Checking Claims Against Wikipedia
Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
The rise of Internet has made it a major source of information. Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched…
Transformers: "The End of History" for NLP?
Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natura…
aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
We 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, addit…