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ACL 2018 paper: Probabilistic FastText for Multi-Sense Word Embeddings (Athiwaratkun et al., 2018)


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Probabilistic FastText for Multi-Sense Word Embeddings

This repository contains the implementation of the models in Athiwaratkun et al., Probabilistic FastText for Multi-Sense Word Embeddings, ACL 2018. The paper can be accessed on Arxiv here.

Similar to our previous work in Athiwaratkun and Wilson, Multimodal Word Distributions, ACL 2017, we represent each word in the dictionary as a Gaussian Mixture distribution that can extract multiple meanings. We use FastText as our subword representation to enhance semantic estimation of rare words or words outside the training vocabulary.

The BibTeX entry for the paper is:

    author = {Ben Athiwaratkun, Andrew Gordon Wilson, and Anima Anandkumar},
    title = {Probabilistic FastText for Multi-Sense Word Embeddings},
    booktitle = {Conference of the Association for Computational Linguistics (ACL)},
    year = {2018}

0. What's in this Library?

We provide

(1) scripts to train the multi-sense FastText embeddings. We give instructions on how to train the model in 1.

(2) Python scripts to evaluate the trained models on word similarity in 2. Our scripts allows the subword model to be loaded directly into a Python object which can be used to other tasks.

(3) pre-trained model and evaluation script in 3. This section includes intructions on how to load a pre-trained FastText model (single sense) into our format which allows loading as Python object.

1. Train

1.1 Compile the C++ files. The step requires a compiler with C++11 support such as g++-4.7.2 or newer or clang-3.3 or newer. It also requires make which can be installed via sudo apt-get install build-essential on Ubuntu.

Once you have make and a C++ compiler, you can compile our code by executing:


This command will generate multift, an executable of our model.

1.2 Obtain text data for training. We included scripts to download text8 and text9 in data/.

bash data/
bash data/

In our paper, we use the concatenation of ukWaC and WaCkypedia_EN as our English text corpus. Both datasets can be requested here.

The foreign language datasets deWac (German), itWac (Italian), and frWac (French) can be requested using the above link as well.

1.3 Run sample training scripts for text8 or text9.

bash exps/

After the training is complete, the following files will be saved:

modelname.words         List of words in the dictionary
modelname.bin           A binary file for the subword embedding model            The subword embeddings
modelname.in2           The embeddings for the second Gaussian component.
modelname.subword       The final representation of words in the dictionary. Note that the representation for words outside the dictionary can be computed using the provided python module based on the files *.in and *.in2.

2. Evaluate

2.1 The provided python module can be used to load the multisense FT object.

ft = multift.MultiFastText(basename="modelfiles/modelname", multi=True)

Note that the first time it loads the model can be quite slow. However, it saves the .npy files for later use which allows the loading to be much faster.

We can query for nearest neighbors give a word or evaluate the embeddings against word similarity datasets.

2.2 The script eval/ calculates the Spearman's correlation for multiple word similarity datasets given a model. We provide examples below.

python eval/ --modelname modelfiles/multi_text8_e10_d300_vs2e-4_lr1e-5_margin1 | tee log/eval_wordsim_text8.txt
python eval/ --modelname modelfiles/multi_text9_e10_d300_vs2e-4_lr1e-5_margin1 | tee log/eval_wordsim_text9.txt

Sample output of the text8. sub and sub2 correspond to the Spearman's correlation of the first and second Gaussian components. We can see that having two components with potentially disentangled meanings improve the Spearman's correlation for word similarity.

Below is the output for word similarity evaluation on text8.

   Dataset        sub       sub2  sub-maxsim  
0       SL  26.746543  10.859781   27.699946  
1       WS  65.720245  34.064534   66.638705  
2     WS-S  70.978888  36.393886   70.474031  
3     WS-R  62.104036  34.294508   60.655725  
4      MEN  63.523683  34.983555   68.341550  
5       MC  57.521141  37.650201   66.266134  
6       RG  57.971173  50.623423   58.956847  
7       YP  33.104499  10.518915   38.473373  
8   MT-287  65.407845  45.664255   70.200717  
9   MT-771  52.972120  33.979479   56.735716  
10      RW  36.954597   3.707983   33.639994  

A sample script eval/ show the nearest neighbors of words such as rock, star, and cell where we observe multiple meanings for each word.

python eval/ | tee log/eval_text9_model_nn.txt
Nearest Neighbors for rock, cluster 0
Top highest similarity of rock cl 0
['rock,:0', ..., 'bedrock:0', 'rocky:0', 'rocks,:0', '[[rock:0', ...

Nearest Neighbors for rock, cluster 1
Top highest similarity of rock cl 1
['(band)]],:0', '(band)]]:0', 'songwriters:0', 'songwriter,:0', 'songwriter:0', ...

3. Loading and Analyzing Pre-Trained Models

We provide a pre-trained English model in .tar.7z and .zip format.

  1. 7z option (15 GB download, long extraction time). The .tar.7z file contails .npy files of vectors and .words for the dictionary file.
wget -P modelfiles/
cd modelfiles
7z x -so mv-wacky_e10_d300_vs2e-4_lr1e-5_mar1.tar.7z | tar xf - -C .
cd ..
  1. .zip option (30 GB, faster extraction time).
wget -P modelfiles/
cd modelfiles
cd ..

3.1 Replicating our paper's results

Evaluate the downloaded model using:

python eval/ --modelname modelfiles/mv-wacky_e10_d300_vs2e-4_lr1e-5_mar1 --multi 1 | tee log/eval_wordsim_multift300_eng.txt

Below is the expected output:

   Dataset        sub       sub2  sub-maxsim
0       SL  37.338851  17.488524   39.605635
1       WS  65.360961  28.699307   76.114355
2     WS-S  71.203755  29.717879   80.114966
3     WS-R  62.165077  35.096523   75.345368
4      MEN  73.473605  30.225000   79.653409
5       MC  77.280821  35.603027   80.930131
6       RG  78.106458  21.979871   79.811171
7       YP  54.495191  13.808997   54.929330
8   MT-287  66.591830  26.882829   69.437580
9   MT-771  58.283664  36.931110   69.678762
10      RW  47.873384  -3.224643   49.358893

3.2 Analyze FastText Objects in Python

FastText ( provide model files in two formats: .bin and .vec. Note that the model based on .bin can calculate the representation for any given word that might not be in the directionary. This has the advantage over using .vec files which contains pre-calculated vectors of words in the training dictionary.

We additionally provide a functionality to convert the .bin FastText objects to our format, which can then be loaded into a Python object using

One can convert a .bin file into our format via:

./multift output-model -multi 0 modelfiles/downloaded_model.bin 

The model in our format will be saved in modelfiles/, modelfiles/downloaded_model.out, etc. This model can be loaded with our Python script using:

ft = multift.MultiFastText(basename=modelfiles/downloaded_model, multi=False)

Note that the above two steps will generate extra .npy files which allow for much faster loading at subsequent times.

Evaluating FastText

The following script downloads the Wiki English embeddings from, converts it to the python-readable format, and evaluate it on word similarity datasets. -P modelfiles/
cd modelfiles
cd ..
./multift output-model -multi 0 modelfiles/wiki.en.bin
python eval/ --modelname modelfiles/wiki.en --multi 0 | tee log/eval_wordsim_ft_wiki-eng.txt


   Dataset  modelfiles/wiki.en-sub
0       SL               38.033168
1       WS               73.880820
2     WS-S               78.111959
3     WS-R               68.201896
4      MEN               76.367311
5       MC               81.197154
6       RG               79.983827
7       YP               53.327914
8   MT-287               67.934178
9   MT-771               66.892286
10      RW               48.092870

Note: The C++ code is adapted from the FastText library (


ACL 2018 paper: Probabilistic FastText for Multi-Sense Word Embeddings (Athiwaratkun et al., 2018)







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