# The Case for Evaluating Multimodal Translation Models on Text Datasets

arXiv: https://arxiv.org/abs/2403.03014 · HTML: https://mlsanity.com/paper/2403.03014/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Probing the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/1903.08678/) (2019)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/2105.14462/) (2021)
- [Incorporating Global Visual Features into Attention-Based Neural Machine Translation](https://mlsanity.com/paper/1701.06521/) (2017)
- [VALHALLA: Visual Hallucination for Machine Translation](https://mlsanity.com/paper/2206.00100/) (2022)
- [Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation](https://mlsanity.com/paper/2212.10140/) (2022)
- [LIUM-CVC Submissions for WMT17 Multimodal Translation Task](https://mlsanity.com/paper/1707.04481/) (2017)

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

- [Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies](https://mlsanity.com/paper/2408.11327/) (2024)

## Abstract (arXiv)

A good evaluation framework should evaluate multimodal machine translation (MMT) models by measuring 1) their use of visual information to aid in the translation task and 2) their ability to translate complex sentences such as done for text-only machine translation. However, most current work in MMT is evaluated against the Multi30k testing sets, which do not measure these properties. Namely, the use of visual information by the MMT model cannot be shown directly from the Multi30k test set results and the sentences in Multi30k are are image captions, i.e., short, descriptive sentences, as opposed to complex sentences that typical text-only machine translation models are evaluated against. Therefore, we propose that MMT models be evaluated using 1) the CoMMuTE evaluation framework, which measures the use of visual information by MMT models, 2) the text-only WMT news translation task test sets, which evaluates translation performance against complex sentences, and 3) the Multi30k test sets, for measuring MMT model performance against a real MMT dataset. Finally, we evaluate recent MMT models trained solely against the Multi30k dataset against our proposed evaluation framework and demonstrate the dramatic drop performance against text-only testing sets compared to recent text-only MT models.