UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers

2023-09-28 · Andrei Paraschiv, Mihai Dascalu

Conspiracy theories have become a prominent and concerning aspect of online discourse, posing challenges to information integrity and societal trust. As such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA 2023 shared task. The combination of pre-trained sentence Transformer models and data augmentation techniques enabled us to secure first place in the final leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in the binary classification and 91.23% for the fine-grained conspiracy topic classification, surpassing other competing systems.

📄 PDF Abstract BibTeX arXiv:2309.16275

Code (0)

등록된 구현이 없습니다.

Tasks

Binary Classification Classification Data AugmentationSentenceTopic Classification

Methods 이 논문이 사용한 방법론

Attention 설명 없음

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

Position-Wise Feed-Forward Layer 설명 없음

Residual Connection 설명 없음

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

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…

Bangla Grammatical Error Detection Using T5 Transformer Model

2023-03-19 · H. A. Z. Sameen Shahgir, Khondker Salman Sayeed

This paper presents a method for detecting grammatical errors in Bangla using a Text-to-Text Transfer Transformer (T5) Language Model, using the small variant of BanglaT5, fine-tuned on a corpus of 9385 sentences where e…

Grammatical Error Detection Language Modeling Language Modelling model+1

ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets

2021-04-01 · EACL (WANLP) 2021 4 · Laila Bashmal, Daliyah AlZeer

Detecting Sarcasm has never been easy for machines to process. In this work, we present our submission of the sub-task1 of the shared task on sarcasm and sentiment detection in Arabic organized by the 6th Workshop for Ar…

Contrastive Learning Sarcasm Detection Sentence Sentence Embedding+1

Holistic Sentence Embeddings for Better Out-of-Distribution Detection

2022-10-14 · Sishuo Chen, Xiaohan Bi, Rundong Gao, Xu sun

Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown s…

Avg Out-of-Distribution Detection Out of Distribution (OOD) Detection Sentence+3

An LSTM Adaptation Study of (Un)grammaticality

2019-08-01 · WS 2019 8 · Shammur Absar Chowdhury, Roberto Zamparelli

We propose a novel approach to the study of how artificial neural network perceive the distinction between grammatical and ungrammatical sentences, a crucial task in the growing field of synthetic linguistics. The method…

CoLA Language Modeling Language Modelling

Online antisemitism across platforms

2021-12-14 · Tom De Smedt

We created a fine-grained AI system for the detection of antisemitism. This Explainable AI will identify English and German anti-Semitic expressions of dehumanization, verbal aggression and conspiracies in online social …

Decision Making