# UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

arXiv: https://arxiv.org/abs/1809.01479 · HTML: https://mlsanity.com/paper/1809.01479/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 30 of 60)

- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Do Androids Know They're Only Dreaming of Electric Sheep?](https://mlsanity.com/paper/2312.17249/) (2023)
- [DialFact: A Benchmark for Fact-Checking in Dialogue](https://mlsanity.com/paper/2110.08222/) (2021)
- [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://mlsanity.com/paper/2108.11357/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Synthetic Disinformation Attacks on Automated Fact Verification Systems](https://mlsanity.com/paper/2202.09381/) (2022)
- [GERE: Generative Evidence Retrieval for Fact Verification](https://mlsanity.com/paper/2204.05511/) (2022)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [The Role of Context in Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2104.07423/) (2021)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification](https://mlsanity.com/paper/2106.01191/) (2021)
- [A Multi-Level Attention Model for Evidence-Based Fact Checking](https://mlsanity.com/paper/2106.00950/) (2021)
- [RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media](https://mlsanity.com/paper/2210.06331/) (2022)
- [Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction](https://mlsanity.com/paper/2207.14116/) (2022)
- [Exploring Decomposition for Table-based Fact Verification](https://mlsanity.com/paper/2109.11020/) (2021)
- [Calibration of Machine Reading Systems at Scale](https://mlsanity.com/paper/2203.10623/) (2022)
- [Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification](https://mlsanity.com/paper/2212.05276/) (2022)
- [BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification](https://mlsanity.com/paper/2303.16974/) (2023)
- [REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy](https://mlsanity.com/paper/2406.07735/) (2024)
- [Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs](https://mlsanity.com/paper/2312.11785/) (2023)
- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification](https://mlsanity.com/paper/2109.12349/) (2021)
- [Data-Efficient Autoregressive Document Retrieval for Fact Verification](https://mlsanity.com/paper/2211.09388/) (2022)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [PASTA: A Dataset for Modeling Participant States in Narratives](https://mlsanity.com/paper/2208.00329/) (2022)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Adversarial Attacks Against Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2509.08463/) (2025)

## Abstract (arXiv)

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).