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Found 18 papers, 12 papers with code
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Clarifying the research framing of NLP artefacts (e. g., models, datasets, etc.)
Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs
The DecompGen feedback is used to automatically construct our preference dataset, DGPref.
Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking
Current automated fact-checking (AFC) approaches commonly evaluate evidence either implicitly via the predicted verdicts or by comparing retrieved evidence with a predefined closed knowledge source, such as Wikipedia.
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers.
Generating Media Background Checks for Automated Source Critical Reasoning
Indeed, retrieval-augmented models are not typically expected to distrust retrieved documents.
Document-level Claim Extraction and Decontextualisation for Fact-Checking
Selecting which claims to check is a time-consuming task for human fact-checkers, especially from documents consisting of multiple sentences and containing multiple claims.
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim.
Multimodal Automated Fact-Checking: A Survey
In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation.
The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation.
A Survey on Automated Fact-Checking
Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem.
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
Joint Verification and Reranking for Open Fact Checking Over Tables
Structured information is an important knowledge source for automatic verification of factual claims.
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases.
Ranked #1 on
Open-Domain Question Answering
on WebQuestions
(using extra training data)
Knowledge Base Question Answering
Open-Domain Question Answering
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Attribution methods assess the contribution of inputs to the model prediction.
Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages
In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding.
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Ranked #1 on
Node Classification
on AIFB
