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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources

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# Post-Specialisation
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources (Vulić et al., NAACL-HLT 2018)

This repository contains the code and data for the post-specialisation method in the NAACL-HLT 2018 paper. The method is implemented in [Keras](https://keras.io/) (Python 2.7)

Contact: Ivan Vulić (iv250@cam.ac.uk)

### Configuring the Tool

The post-specialisation tool reads all the experiment config parameters from the ```experiment_parameters.cfg``` file in the root directory. An alternative config file can be provided as the first (and only) argument to ```code/post-specialisation.py```.

Note that the tool assumes that input distributional vectors have already been initially specialised with one of the standard specialisation models such as [retrofitting](https://github.com/mfaruqui/retrofitting) (Faruqui et al., NAACL-HLT 2015), [counter-fitting](https://github.com/nmrksic/counter-fitting) (Mrkšić et al., NAACL-HLT 2016), or [Attract-Repel](https://github.com/nmrksic/attract-repel) (Mrkšić et al., TACL 2017). In our experiments, we use the most recent and state-of-the-art post-processor Attract-Repel, but the post-specialisation model is equally applicable to any other post-processor. If you use any of these tools, please cite the corresponding paper(s).

The config file specifies the following:
1. The location of the initial word vectors (```distributional_vectors```)
- In the default setup, we use SGNS vectors with bag-of-words contexts trained on Wikipedia, available [here](http://u.cs.biu.ac.il/~yogo/data/syntemb/bow2.words.bz2)
* the location of the training data; training data contains word vectors (x_i, x_o) changed by the initial specialisation (i.e., seen words x)
2. We have to specify the location of the distributional vectors (x_i) in ```distributional_training_data``` as well as the location of the specialised vectors (x_o) in ```specialised_training_data```
- The two training data files follow the standard format for word vectors (word dim_1 dim_2 ... dim_N), but note that they have to contain representations of exactly the same words and have exactly the same number of items.
- We have provided toy sample training data files containing 5000 training pairs to illustrate the data format.
3. The config file also specifies the hyperparameters of the post-specialisation procedure (set to their default values in ```config/experiment_parameters.cfg```).
- Right now, we provide support in the config file only for the best-performing max-margin loss function, but it should be fairly easy to customise the objective function using some of the Keras pre-built losses.

### Running Experiments

```python code/post-specialisation.py config/experiment_parameters.cfg```

Running the experiment loads the word vectors specified in the config file and learns the mapping/regression function using a deep feed-forward network as specified in the config file. The procedure prints the updated word vectors to the results directory as ```results/final_vectors.txt``` (standard format: one word vector per line)

### References

The paper which introduces the Post-Specialisation procedure and the problem of specialising unseen words:
```
 @inproceedings{Vulic:2018,
  author    = {Ivan Vuli\'{c} and Goran Glava\v{s} and Nikola Mrk\v{s}i\'c} and Anna Korhonen,
  title     = {Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources},
  booktitle   = {Proceedings of NAACL-HLT},
  year      = 2018,
 }
```

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