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Found 19 papers, 7 papers with code
Explaining Interactions Between Text Spans
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI).
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
Faithfulness Tests for Natural Language Explanations
Explanations of neural models aim to reveal a model's decision-making process for its predictions.
Accountable and Explainable Methods for Complex Reasoning over Text
A major concern of Machine Learning (ML) models is their opacity.
Fact Checking with Insufficient Evidence
To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions.
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
In addition, we show the applicability of our approach in a completely unsupervised setting.
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims
We present an overview of the second edition of the CheckThat!
Diagnostics-Guided Explanation Generation
When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model.
A Diagnostic Study of Explainability Techniques for Text Classification
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity.
Generating Label Cohesive and Well-Formed Adversarial Claims
However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in.
Multi-Hop Fact Checking of Political Claims
We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.
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 Multilingual Offensive Language Identification in Social Media (OffensEval 2020).
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression.
Generating Fact Checking Explanations
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.
Joint Emotion Label Space Modelling for Affect Lexica
Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection.
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-worthiness of claims in political debates.
Automatic Fact-Checking Using Context and Discourse Information
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.
Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search
While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
For subtask A, all systems improved over the majority class baseline.
