This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts》 along with the code to reproduce results in the paper (See Section Baselines).


When humans converse, what a speaker will say next significantly depends on what he sees. OpenViDial is a largescale multi-module dialogue dataset for this purpose. The dialogue turns and visual contexts are extracted from movies and TV series, where each dialogue turn is paired with the corresponding visual context in which it takes place. OpenViDial contains a total number of 1.1 million dialogue turns, and thus 1.1 million visual contexts stored in images.

The following are two short conversations where visual contexts are crucial.

Detailed statistics for OpenViDial

Attribute value
Number of turns 1.1M
Number of images 1.1M
Vocab size before BPE 70K
Vocab size after BPE 30K
Average length of each episode 14
Average length of each turn 7.6

Download the Dataset

The main folder origin_dir contains training/valid/test sets, each of which is made up by the following files:

      └── train.dialogue.jsonl // each line is an episode of dialogue, which a list of IDs.    
      └── train.origin.txt // each line corresponds to a dialogue text utterence, with the ID being its line number (staring with 0).
      └── train_images // containing images (visual contexts) in which the text utterence take place, with ID being the image filename (0,1,2, etc)
            └── 0.jpg
            └── 1.jpg
            └── ...
      └── valid.* (i.e., valid.dialogue.jsonl, valid.origin.txt, valid_images)
      └── test.*  (i.e., test.dialogue.jsonl, test.origin.txt, test_images)

If you’d like to take a glance at the a sample of the dataset instead of downloading the full dataset, we provide a data sample here

Data download:

  1. Download [train|valid|test].origin.txt and [train|valid|test].dialogue.jsonl here
  2. Download test_images (~ 20G) here
  3. Download valid_images (~ 20G) here
  4. Download train_images: Since train_images is too big (~ 170G), we split it to 11 zip files (each of which is 17G). Download seperate files zip_train here. Then download and run here to include all files in the same directory.
  5. Move all files to origin_dir.


We proposed three models for this dataset. Please refer to the paper for details:

  • Model #1 – NoVisual: use only dialog texts without visual information
  • Model #2 – CoarseVisual: use texts and a pretrained ResNet50 on ImageNet to compute 1000-d feature from each picture
  • Model #3 – FineVisual: use texts and a pretrained Faster R-CNN on Genome to compute 2048-d * K objects features from each picture

Faster R-CNN is an object detection framework. The detection sample and attention over objects during text decoding is shown below.


  • python >= 3.6
  • pip install -r requirements.txt

Preprocess directory structure

preprocessed_data_dir is a directory that contains all the preprocessed files (text, image feature mmap, offsets, etc.) generated from origin_data_dir and we use them in training models. The directory structure is shown below.

Note: every train* file or directory should have a ‘valid’ and a ‘test’ counterpart, we ignore them below for simplicity.

      └── train.features.mmap  // numpy mmap array file of shape [num_sents, 1000], each row is a 1000-d ResNet-50 feature
      └── train.objects.mmap  // numpy mmap array file of shape [num_sents, 20, 2048],  faster-rcnn object feature file, each row contain 20 objects feature, which is 2048-d
      └── train.objects_mask.mmap  // numpy mmap array file of shape [num_sents, 20],  faster-rcnn mask file, each row contain 20 objects mask, 1 for valid, 0 for mask
      └── train.offsets.npy  // numpy array file of shape [num_episodes], each item is the offsets of one episode
      └── train.sent_num.npy // numpy array file of shape [num_episodes], each item is the sentence number of one episode

Preprocess text data

We use Moses Tokenizer to tokenize texts and generate offsets arrays: bash ./scripts/ and followed with byte-pair-encoding and fairseq-preprocess binarization: bash ./scripts/

Note: You need to change DATA_DIR, ORIGIN_DIR, OUTPUT_DIR to your own path

Prepare pre-computed CNN features and Faster-RCNN features

Download CNN-pooling features(Used for Model #2 – CoarseVisual)

Preprocessed ResNet50 features (*.features.mmap) (~4G) can be downloaded from here and move them under preprocessed_data_dir/

Download Faster R-CNN features(Used for Model #3 – FineVisual)

Preprocessed Faster R-CNN objects features (*objects.mmap, *objects_mask.mmap) (~160G) can be downloaded from here then move them under preprocessed_data_dir/

Since file train.objects.mmap is too large(100G+), we splitted it to many small pieces like train.objects.mmap.split*, and you need another step to merge all those files together: cat * train.objects.mmap.split* >train.objects.mmap

(Optional) Extract features on your own

If you want to extract some feature on your own, or you’d like to know details of extracting visual features, see video_dialogue_model/extract_features/

Train and Evaluate Model #1 – NoVisual

bash scripts/reproduce_baselines/ will train and evaluate NoVisual, Remember to change MODEL_DIR and DATA_DIR for your setup

Train and Evaluate Model #2 – CoarseVisual

bash scripts/reproduce_baselines/ will train and evaluate CoarseVisual. Remember to change MODEL_DIR and DATA_DIR for your setup

Train and Evaluate Model #3 – FineVisual

bash scripts/reproduce_baselines/ will train and evaluate FineVisual, Remember to change MODEL_DIR and DATA_DIR for your setup

Other Statistics

  • get length/diversity/stopwords% statistics of system output: train/

Model benchmark

Model BLEU-1 BLEU-2 BLEU-4 Stopword% Dis-1 Dis-2 Dis-3 Dis-4
1-NV 14.01 3.98 1.07 58.1% 0.0091 0.0355 0.0682 0.1018
2-CV 14.58 4.35 1.14 54.2% 0.0108 0.0448 0.0915 0.1465
3-FV 15.61 4.71 1.22 52.9% 0.0118 0.0502 0.1082 0.1778