Pepa Atanasova | DeepAI

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∙ 05/29/2023

Faithfulness Tests for Natural Language Explanations

Explanations of neural models aim to reveal a model's decision-making pr...

0 Pepa Atanasova, et al. ∙

∙ 11/09/2022

Accountable and Explainable Methods for Complex Reasoning over Text

A major concern of Machine Learning (ML) models is their opacity. They a...

0 Pepa Atanasova, et al. ∙

∙ 04/05/2022

Fact Checking with Insufficient Evidence

Automating the fact checking (FC) process relies on information obtained...

0 Pepa Atanasova, et al. ∙

∙ 12/13/2021

Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

Fact-checking systems have become important tools to verify fake and mis...

0 Shailza Jolly, et al. ∙

∙ 09/08/2021

Diagnostics-Guided Explanation Generation

Explanations shed light on a machine learning model's rationales and can...

0 Pepa Atanasova, et al. ∙

∙ 09/25/2020

A Diagnostic Study of Explainability Techniques for Text Classification

Recent developments in machine learning have introduced models that appr...

0 Pepa Atanasova, et al. ∙

∙ 09/17/2020

Generating Label Cohesive and Well-Formed Adversarial Claims

Adversarial attacks reveal important vulnerabilities and flaws of traine...

0 Pepa Atanasova, et al. ∙

∙ 09/10/2020

Multi-Hop Fact Checking of Political Claims

Recently, novel multi-hop models and datasets have been introduced to ac...

5 Wojciech Ostrowski, et al. ∙

∙ 06/12/2020

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

We present the results and main findings of SemEval-2020 Task 12 on Mult...

0 Marcos Zampieri, et al. ∙

∙ 04/29/2020

A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

The use of offensive language is a major problem in social media which h...

0 Sara Rosenthal, et al. ∙

∙ 04/13/2020

Generating Fact Checking Explanations

Most existing work on automated fact checking is concerned with predicti...

12 Pepa Atanasova, et al. ∙

∙ 11/20/2019

Joint Emotion Label Space Modelling for Affect Lexica

Emotion lexica are commonly used resources to combat data poverty in aut...

0 Luna De Bruyne, et al. ∙

∙ 08/19/2019

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

We propose a multi-task deep-learning approach for estimating the check-...

4 Slavena Vasileva, et al. ∙

∙ 08/04/2019

Automatic Fact-Checking Using Context and Discourse Information

We study the problem of automatic fact-checking, paying special attentio...

4 Pepa Atanasova, et al. ∙

∙ 05/25/2019

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

We present SemEval-2019 Task 8 on Fact Checking in Community Question An...

0 Tsvetomila Mihaylova, et al. ∙

∙ 05/25/2019

Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search

In recent years, the proliferation of smart mobile devices has lead to t...

0 Pepa Atanasova, et al. ∙

∙ 08/08/2018

Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness

We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Iden...

0 Pepa Atanasova, et al. ∙

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