# SURFACE: Semantically Rich Fact Validation with Explanations

arXiv: https://arxiv.org/abs/1810.13223 · HTML: https://mlsanity.com/paper/1810.13223/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence](https://mlsanity.com/paper/2401.03188/) (2024)
- [Knowledge Graph Curation: A Practical Framework](https://mlsanity.com/paper/2208.08130/) (2022)
- [Semantically-informed Hierarchical Event Modeling](https://mlsanity.com/paper/2212.10547/) (2022)
- [RevUp: Revise and Update Information Bottleneck for Event Representation](https://mlsanity.com/paper/2205.12248/) (2022)

## Abstract (arXiv)

Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant evidential sentences in the trusted source and (ii) use them to classify the input sentence's veracity. An evaluation of our efficient three-parameter model on the FEVER dataset showed an improvement of 90% over the state-of-the-art baseline on retrieving relevant sentences and a 70% relative improvement in classification.