# A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification

arXiv: https://arxiv.org/abs/2012.14500 · HTML: https://mlsanity.com/paper/2012.14500/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [SciFact-Open: Towards open-domain scientific claim verification](https://mlsanity.com/paper/2210.13777/) (2022)
- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [Synthetic Disinformation Attacks on Automated Fact Verification Systems](https://mlsanity.com/paper/2202.09381/) (2022)
- [Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification](https://mlsanity.com/paper/2110.15116/) (2021)
- [Overview and Insights from the SciVer Shared Task on Scientific Claim Verification](https://mlsanity.com/paper/2107.08188/) (2021)
- [CORWA: A Citation-Oriented Related Work Annotation Dataset](https://mlsanity.com/paper/2205.03512/) (2022)
- [A Multi-Level Attention Model for Evidence-Based Fact Checking](https://mlsanity.com/paper/2106.00950/) (2021)
- [Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection](https://mlsanity.com/paper/2205.10455/) (2022)
- [BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature](https://mlsanity.com/paper/2601.16993/) (2026)
- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification](https://mlsanity.com/paper/2104.11572/) (2021)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)
- [RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification](https://mlsanity.com/paper/2202.02646/) (2022)
- [Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training](https://mlsanity.com/paper/2208.08749/) (2022)
- [MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification](https://mlsanity.com/paper/2401.16282/) (2024)
- [Minimal Evidence Group Identification for Claim Verification](https://mlsanity.com/paper/2404.15588/) (2024)
- [CORRECT: Context- and Reference-Augmented Reasoning and Prompting for Fact-Checking](https://mlsanity.com/paper/2502.09635/) (2025)
- [Detecting Reference Errors in Scientific Literature with Large Language Models](https://mlsanity.com/paper/2411.06101/) (2024)
- [Robust Claim Verification Through Fact Detection](https://mlsanity.com/paper/2407.18367/) (2024)
- [Enhancing Health Fact-Checking with LLM-Generated Synthetic Data](https://mlsanity.com/paper/2508.20525/) (2025)
- [SciClaims: An End-to-End Generative System for Biomedical Claim Analysis](https://mlsanity.com/paper/2503.18526/) (2025)
- [+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking](https://mlsanity.com/paper/2508.11122/) (2025)
- [Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification](https://mlsanity.com/paper/2205.05646/) (2022)
- [Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models](https://mlsanity.com/paper/2503.08495/) (2025)
- [Detection and Interpretability Analysis of Quotation Errors by Large Language Models](https://mlsanity.com/paper/2606.08589/) (2026)

## Abstract (arXiv)

Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction.