# Visuallly Grounded Generation of Entailments from Premises

arXiv: https://arxiv.org/abs/1909.09788 · HTML: https://mlsanity.com/paper/1909.09788/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration](https://mlsanity.com/paper/2205.10593/) (2022)

## Abstract (arXiv)

Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the em generation of hypotheses from premises in a multimodal setting, to generate a sentence (hypothesis) given an image and/or its description (premise) as the input. The main goals of this paper are (a) to investigate whether it is reasonable to frame NLI as a generation task; and (b) to consider the degree to which grounding textual premises in visual information is beneficial to generation. We compare different neural architectures, showing through automatic and human evaluation that entailments can indeed be generated successfully. We also show that multimodal models outperform unimodal models in this task, albeit marginally.