# Cross-lingual Visual Verb Sense Disambiguation

arXiv: https://arxiv.org/abs/1904.05092 · HTML: https://mlsanity.com/paper/1904.05092/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

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

- [Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models](https://mlsanity.com/paper/2109.03415/) (2021)
- [Endowing Language Models with Multimodal Knowledge Graph Representations](https://mlsanity.com/paper/2206.13163/) (2022)
- [BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation](https://mlsanity.com/paper/2305.18326/) (2023)
- [A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges](https://mlsanity.com/paper/2405.12669/) (2024)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories](https://mlsanity.com/paper/2308.10562/) (2023)
- [Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2210.16960/) (2022)
- [Bridging Lexical Ambiguity and Vision: A Mini Review on Visual Word Sense Disambiguation](https://mlsanity.com/paper/2602.01193/) (2026)
- [Scalable Multilingual Multimodal Machine Translation with Speech-Text Fusion](https://mlsanity.com/paper/2602.21646/) (2026)

## Abstract (arXiv)

Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9,504 images annotated with English, German, and Spanish verbs. Each image in MultiSense is annotated with an English verb and its translation in German or Spanish. We show that cross-lingual verb sense disambiguation models benefit from visual context, compared to unimodal baselines. We also show that the verb sense predicted by our best disambiguation model can improve the results of a text-only machine translation system when used for a multimodal translation task.