WAT2019 Multi-Modal Translation Task

In 2019, the Workshop on Asian Translation 2019 (WAT2019) included the task of multimodal English-to-Hindi translation for the first time in its history. The task relies on our “Hindi Visual Genome”, a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research.


  • May 30, 2019: Release of Training Data
  • August 3, 2019: Test week starts, release of source side of the test set
  • August 10, 2019: Test week ends, translations need to be submitted to the organizers
  • September 13, 2019: System description paper submission deadline
  • September 20, 2019: Review feedback for system description
  • September 30, 2019: Camera-ready
  • November 3-4, 2019: WAT2019 takes place

Task Description

The setup of the WAT2019 task is as follows:

  • Inputs:
    • An image,
    • A rectangular region in that image
    • A short English caption of the rectangular region.
  • Output:
    • The caption translated to Hindi.

Types of Submissions Expected

The setup of the WAT2019 task is as follows:

  • Text-only translation
  • Hindi-only image captioning
  • Multi-modal translation (uses both the image and the text)

Training Data

The Hindi Visual Genome consists of:

  • 29k training examples
  • 1k dev set
  • 1.6k evaluation set

The evaluation test set will be released jointly with the release of Hindi Visual Genome but it will serve as the first part of WAT2019 official test set. Therefore, it must not be used by the participants during the training or model selection.


WAT2019 Multi-Modal Task will be evaluated on:

  • 1.6k evaluation set of Hindi Visual Genome (mentioned above)
    • This test set is being released in the HVG package, so remember not to use it in any way.
  • 1.4k challenge set of Hindi Visual Genome        
    • This second part of WAT2019 official test set will be released only at the WAT2019 evaluation week.
    • It nevertheless comes from the original Visual Genome dataset, so participants are requested to indicate whether they consider the original (English-only) Visual Genome dataset in their training.

Means of evaluation:

  • Automatic metrics: BLEU, CHRF3, and others
  • Manual evaluation, subject to the availability of Hindi speakers

Participants of the task need to indicate which track their translations belong to:

  • Text-only / Image-only / Multi-modal
    • see above
  • Domain-Aware / Domain-Unaware
    • Whether the full (English) Visual Genome was used in the training or not.
  • Constrained / Non-Constrained
    • 29k training segments from the Hindi Visual Genome
    • HindEnCorp 0.5
    • (English-only) Visual Genome [making the submission a domain-aware run]
  • Non-constrained submission may use other data, but need to specify what data was used.

Download Link

Submission Requirement

The system description should be a short report (4 to 6 pages) submitted to WAT 2019 describing the method(s).

Each participating team can submit at most 2 systems for each of the task (e.g. Text-only, Hindi-only image captioning, multimodal translation using text and image)


Please refer to the preprint version of the paper:

Human Evaluation Result

Note: Score is the average score in the original 0-100 and *Zscores are scores but first standardized for each annotator across all his/her annotations. Details are be available in the "Overview of the 6th Workshop on Asian Translation" proceeding. Human evaluation interface used by the evaluators.

Report us for any error spotted.

Multimodal Subtask Team DataID Score *ZScore
EVTEXT IDIAP 2956 72.84 0.70
  683 3285 68.89 0.57
  683 3286 61.63 0.36
  NITSNLP 3299 52.53 0.00
CHTEXT IDIAP 3277 59.81 0.22
  IDIAP 3267 59.36 0.22
  683 3287 45.38 -0.24
  683 3284 45.95 -0.26
  NITSNLP 3300 27.91 -0.82
EVHI NITSNLP 3289 51.77 -0.04
CHHI NITSNLP 3297 44.45 -0.34
  683 3304 26.54 -0.94
EVMM 683 3271 69.17 0.60
  NITSNLP 3288 58.98 0.25
  PUP-IND 3296 62.42 0.34
  PUP-IND 3295 60.22 0.27
CHMM 683 3270 54.5 0.08
  NITSNLP 3298 48.45 -0.19
  PUP-IND 3281 48.06 -0.12
  PUP-IND 3280 47.06 -0.16

Organizers Presentation

The overview presentation includes the task, participants, results, and analysis. Presented at EMNLP 2019 Hongkong (WAT Workshop) on 4th Nov 2019. [ppt


  • Ondřej Bojar (Charles University, Czech Republic)
  • Shantipriya Parida (Idiap Research Institute, Switzerland)


email: wat-multimodal-task@ufal.mff.cuni.cz


The data is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.


This shared task is supported by the grant nr. 19-26934X (NEUREM3) of Czech Science Foundation.