---
title: "Multimodal Transformer for Multimodal Machine Translation (2020)"
description: "They propose a Transformer encoder with multimodal self-attention. 0.6 BLEU. 17 references, cited by 56 papers here."
authors: ["Shaowei Yao", "Xiaojun Wan"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.400/"
doi: "10.18653/v1/2020.acl-main.400"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yao-2020-multimodal-transformer-multimodal-machine-translation/"
---
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# Multimodal Transformer for Multimodal Machine Translation

**Authors:** Shaowei Yao and [Xiaojun Wan](https://md.research.elamur.ai/free/en/authors/xiaojun-wan/) · **Year:** 2020 · **Venue:** [Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2020](https://md.research.elamur.ai/free/en/venues/acl/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.400/) · [PDF](https://aclanthology.org/2020.acl-main.400.pdf) · [DOI 10.18653/v1/2020.acl-main.400](https://doi.org/10.18653/v1/2020.acl-main.400) · [OpenAlex](https://openalex.org/W3034773362)

**Citations:** 141 citations (OpenAlex, 2026-10-14) · [56](https://md.research.elamur.ai/free/en/papers/yao-2020-multimodal-transformer-multimodal-machine-translation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses multimodal machine translation (MMT), where an image accompanies the source sentence. The authors argue that text should dominate because raw image features carry much irrelevant content, and treating both inputs equally can add noise. They propose a Transformer encoder with multimodal self-attention. Following a graph view of Transformer, image regions are added as extra pseudo-word nodes next to the words. However, the image nodes' hidden states are built only from the word representations. The visual features serve solely to steer how the attention weights are assigned, so the image is never encoded directly. Experiments use Multi30k English→German (Test2016), with ResNet-50 spatial features, optionally adding back-translated training data. The model beats text-only and earlier multimodal systems on BLEU and reaches a new state of the art once extra data is included. Ablations show the gains come from relevant visual input. The design offers a simple way to let a weaker modality support a stronger one, which may transfer to other unbalanced multimodal tasks.

## Key points
- Data: Multi30k (M30k_T for training; 145k German captions from M30k_C back-translated with an LSTM De→En model). Features are 7×7×2048 grids from ResNet-50's final convolutional layer. Setup uses GloVe 300-d embeddings and 6 encoder plus 6 decoder layers with 10 heads.
- On original data, the model scores 38.7 BLEU4 and 55.7 METEOR. This beats the text-only Transformer (37.8/55.3) and the best prior BLEU (Del+obj, 38.0), while VMMT_F keeps a slightly higher METEOR (56.0).
- With back-translated data, the model reaches 39.5 BLEU4 and 56.9 METEOR, surpassing VMMT_F (38.4/56.3) and IMG_D (38.5/55.9).
- Ablation: swapping multimodal self-attention for plain self-attention over concatenated word and image features costs 0.6 BLEU and 0.4 METEOR. Blank images reduce scores to 37.1/54.8, and random images reduce them to 36.7/54.5, which is below the text-only baseline.
- Attention visualizations indicate that heads focus on caption-relevant image regions, such as a building or a balance beam, which helps resolve translation ambiguities.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 100 works · 35,618 citations

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🧭 Topics (2): [🔁 Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) · 958 works · [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 269 works

## Cite

Cite as: Shaowei Yao and Xiaojun Wan. 2020. Multimodal Transformer for Multimodal Machine Translation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yao2020multimodal,
  title = {{Multimodal Transformer for Multimodal Machine Translation}},
  author = {Shaowei Yao and Xiaojun Wan},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.400},
  url = {https://doi.org/10.18653/v1/2020.acl-main.400}
}
```

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## References

17 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Loı̈c Barrault, Fethi Bougares, Lucia Specia, Chiraag Lala, Desmond Elliott, and Stella Frank. 2018. [Findings of the third shared task on multimodal machine translation](https://md.research.elamur.ai/free/en/papers/barrault-2018-findings-third-shared-task-multimodal/). In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 304–323.
2. Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018. [Relational inductive biases, deep learning, and graph networks](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/). arXiv preprint arXiv:1806.01261.
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7. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In ICLR. [find](https://scholar.google.com/scholar?q=Diederik+P+Kingma+and+Jimmy+Ba%2E+2014%2E+Adam%3A+A+method+for+stochastic+optimization%2E+In+ICLR%2E)
8. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In ACL, pages 311– 318.
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10. Lucia Specia, Stella Frank, Khalil Simaan, and Desmond Elliott. 2016. [A shared task on multimodal machine translation and crosslingual image description](https://md.research.elamur.ai/free/en/papers/specia-2016-shared-task-multimodal-machine-translation/). In Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers, pages 543–553.
11. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In NIPS, pages 5998–6008.
12. Shaowei Yao, Tianming Wang, and Xiaojun Wan. 2020. Heterogeneous graph transformer for graph-to-sequence learning. In Proceedings of the Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Shaowei+Yao%2C+Tianming+Wang%2C+and+Xiaojun+Wan%2E+2020%2E+Heterogeneous+graph+transformer+for+graph%2Dto%2Dsequence+learning%2E+In+Proceedings+of+the+Association+for+Computational+Linguistics+%28ACL%29%2E)
13. Iacer Calixto, Miguel Rios, and Wilker Aziz. 2019. Latent variable model for multi-modal translation. In ACL, pages 6392–6405. [find](https://scholar.google.com/scholar?q=Iacer+Calixto%2C+Miguel+Rios%2C+and+Wilker+Aziz%2E+2019%2E+Latent+variable+model+for+multi%2Dmodal+translation%2E+In+ACL%2C+pages+6392%E2%80%936405%2E)
14. Michael Denkowski and Alon Lavie. 2014. [Meteor universal: Language specific translation evaluation for any target language](https://md.research.elamur.ai/free/en/papers/denkowski-2014-meteor-universal-language-specific-translation/). In Proceedings of the Ninth Workshop on Statistical Machine Translation, pages 376–380.
15. Desmond Elliott. 2018. Adversarial evaluation of multimodal machine translation. In EMNLP, pages 2974–2978. [find](https://scholar.google.com/scholar?q=Desmond+Elliott%2E+2018%2E+Adversarial+evaluation+of+multimodal+machine+translation%2E+In+EMNLP%2C+pages+2974%E2%80%932978%2E)
16. Desmond Elliott, Stella Frank, Loı̈c Barrault, Fethi Bougares, and Lucia Specia. 2017. [Findings of the second shared task on multimodal machine translation and multilingual image description](https://md.research.elamur.ai/free/en/papers/elliott-2017-findings-second-shared-task-multimodal/). In Proceedings of the Second Conference on Machine Translation, pages 215–233.
17. Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia. 2016. [Multi30K: Multilingual English-German image descriptions](https://md.research.elamur.ai/free/en/papers/elliott-2016-multi30k-multilingual-english-german-image/). In Proceedings of the 5th Workshop on Vision and Language, pages 70– 74.

*Updated 24 October 2026*