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Dynamic Meta-Embeddings for Improved Sentence Representations
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Dynamic Meta-Embeddings for Improved Sentence Representations

Code and models for the paper Dynamic Meta-Embeddings for Improved Sentence Representations.


  • Python 2.7 or 3.6+
  • PyTorch >= 0.4.1
  • torchtext >= 0.2.3
  • torchvision >= 0.2.1
  • Spacy >= 2.0.11
  • NumPy >= 1.14.0
  • jsonlines
  • tqdm
  • six

Getting started

Downloading the data

First, you should get pre-trained embeddings and pre-processed datasets in place. For embeddings, run

python --embeds fasttext,glove

(An example for fasttext and glove. Available embeddings are fasttext, fasttext_wiki, fasttext_opensubtitles, fasttext_torontobooks, glove, levy_bow2 and imagenet.)

For Flickr30k dataset, run

python --flickr30k_root './data/flickr30k' --batch_size 32

with specified batch size for image feature extraction and Flickr30k root folder that includes dataset_flickr30k.json and images subfolder for all images.

For SNLI/MultiNLI/SST dataset, run, and, respectively.

The downloaded embedding and datasets will be located at ./data/embeddings and ./data/datasets, respectively.

Training the models

Then, you can train the model by running

python [arguments...]

Arguments are listed as follows:

  --name NAME           experiment name
  --task {snli,multinli,allnli,sst2,flickr30k}
                        task to train the model on
  --datasets_root DATASETS_ROOT
                        root path to dataset files
  --embeds_root EMBEDS_ROOT
                        root path to embedding files
  --savedir SAVEDIR     root path to checkpoint and caching files
  --batch_sz BATCH_SZ   minibatch size
  --clf_dropout CLF_DROPOUT
                        dropout in classifier MLP
  --early_stop_patience EARLY_STOP_PATIENCE
                        patience in early stopping criterion
  --grad_clip GRAD_CLIP
                        gradient clipping threshold
  --lr LR               learning rate
  --lr_min LR_MIN       minimal learning rate
  --lr_shrink LR_SHRINK
                        learning rate decaying factor
  --max_epochs MAX_EPOCHS
                        maximal number of epochs
  --optimizer {adam,sgd}
  --resume_from RESUME_FROM
                        checkpoint file to resume training from (default is
                        the one with same experiment name)
  --scheduler_patience SCHEDULER_PATIENCE
                        patience in learning rate scheduler
  --seed SEED           random seed
  --attnnet {none,no_dep_softmax,dep_softmax,no_dep_gating,dep_gating}
                        the attention type
  --emb_dropout EMB_DROPOUT
                        the dropout in embedder
  --proj_embed_sz PROJ_EMBED_SZ
                        dimension of projected embeddings (default is the
                        smallest dimension out of all embeddings)
  --embeds EMBEDS       pre-trained embedding names
  --mixmode {cat,proj_sum}
                        method of combining embeddings
  --nonlin {none,relu}  nonlinearity in embedder
  --rnn_dim RNN_DIM     dimension of RNN sentence encoder
  --fc_dim FC_DIM       hidden layer size in classifier MLP
  --img_cropping {1c,rc}
                        image cropping method (1c/rc: center/random cropping)
                        in image caption retrieval task
  --img_feat_dim IMG_FEAT_DIM
                        image feature size in image caption retrieval task
  --margin MARGIN       margin in ranking loss for image caption retrieval

Here is an example for training SNLI model using fastText and glove embeddings:

python --task snli \
--datasets_root data/datasets --embeds_root data/embeddings --savedir checkpoints \
--embeds fasttext,glove --mixmode proj_sum --attnnet no_dep_softmax \
--nonlin relu --rnn_dim 128 --fc_dim 128 \
--optimizer adam --lr 0.0004 --lr_min 0.00008 --batch_sz 64 --emb_dropout 0.2 --clf_dropout 0.2

Allowing more types of embeddings

To allow using new types of embeddings in training, put the embedding files into data/embeddings. Then update the embeddings list in dme/ with a new tuple per new type of embeddings. Each tuple will provide the id of the embeddings, the embedding filename, the dimensionality, a description and the downloading URL (optional).

Pre-trained models


--batch_sz 64 --clf_dropout 0.2 --lr 0.0004 --lr_min 0.00008 --emb_dropout 0.2 --proj_embed_sz 256 --embeds fasttext,glove --rnn_dim 512 --fc_dim 1024

DME (Accuracy: 86.9096%) / CDME (Accuracy: 86.6042%)


--batch_sz 64 --clf_dropout 0.2 --lr 0.0004 --lr_min 0.00008 --emb_dropout 0.2 --proj_embed_sz 256 --embeds fasttext,glove --rnn_dim 512 --fc_dim 1024

DME (Accuracy: 74.3084%) / CDME (Accuracy: 74.7152%)


--batch_sz 64 --clf_dropout 0.5 --lr 0.0004 --lr_min 0.00005 --emb_dropout 0.5 --proj_embed_sz 256 --embeds fasttext,glove --rnn_dim 512 --fc_dim 512

DME (Accuracy: 89.5113%) / CDME (Accuracy: 88.1933%)


--batch_sz 128 --clf_dropout 0.1 --early_stop_patience 5 --lr 0.0003 --lr_min 0.00005 --scheduler_patience 1 --emb_dropout 0.1 --proj_embed_sz 256 --embeds fasttext,imagenet --rnn_dim 1024 --fc_dim 512 --img_cropping rc

DME (Cap/Img R@1=47.3/33.12, R@10=80.9/73.44) / CDME (Cap/Img R@1=48.2/34.5, R@10=82.3/73.58)


--batch_sz 64 --clf_dropout 0.2 --lr 0.0004 --lr_min 0.00008 --emb_dropout 0.2 --proj_embed_sz 256 --embeds fasttext,glove --rnn_dim 2048 --fc_dim 1024

DME (Accuracy: 80.2757%) / CDME (Accuracy: 80.4742%)


Please cite the following paper if you find this code useful in your research:

D. Kiela, C. Wang, K. Cho, Dynamic Meta-Embeddings for Improved Sentence Representations

  title={Dynamic Meta-Embeddings for Improved Sentence Representations},
  author={Kiela, Douwe and Wang, Changhan and Cho, Kyunghyun},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  address={Brussels, Belgium},


This code is licensed under CC-BY-NC 4.0.

We use SNLI, MultiNLI, SST and Flickr30k datasets in the experiments. Please check their websites for license and citation information.


This repo is maintained by Changhan Wang ( and Douwe Kiela (

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