# Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

arXiv: https://arxiv.org/abs/1603.08079 · HTML: https://mlsanity.com/paper/1603.08079/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2016.

## Built on by (most cited first, 9 of 9)

- [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://mlsanity.com/paper/2306.00824/) (2023)
- [Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models](https://mlsanity.com/paper/2310.05861/) (2023)
- [FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing](https://mlsanity.com/paper/2503.19191/) (2025)
- [RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs](https://mlsanity.com/paper/2412.13835/) (2024)
- [Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models](https://mlsanity.com/paper/2211.12503/) (2022)
- [Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!](https://mlsanity.com/paper/2402.12486/) (2024)
- [EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenarios](https://mlsanity.com/paper/2204.02905/) (2022)
- [Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models](https://mlsanity.com/paper/2512.00349/) (2025)
- [LaViSA: A Language and Vision Structural Ambiguity Benchmark](https://mlsanity.com/paper/2606.19552/) (2026)

## Abstract (arXiv)

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.