Awesome

Counter-fitting Word Vectors to Linguistic Constraints

This repository contains the code and data for the method presented in Counter-fitting Word Vectors to Linguistic Constraints. The word vectors which achieve the (present) state of the art (0.74) on the SimLex-999 dataset are included in this repository.

###Configuring the Tool

The counter-fitting 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 counterfitting.py.

The config file specifies:

the location of the initial word vectors [default: word_vectors/glove.txt]
the vocabulary to be used [default: lingustic_constraints/vocabulary.txt]
the sets of linguistic constraints to be injected into the vector space. The linguistic_constraints directory contains the synonymy (PPDB 2.0) and antonymy (WordNet and PPDB 2.0) constraints used in our experiments.
optionally, one can also specify the location of a dialogue domain ontology (in the DSTC format). This ontology will be used to infer additional antonymy constraints between slot values. The linguistic_constraints directory contains the two dialogue ontologies (DSTC2, DSTC3) used in our experiments.

The config file also specifies the six hyperparameters of the counter-fitting procedure (set to their default values in experiment_parameters.cfg).

The results directory also contains the SimLex-999 dataset (Hill et al., 2014), required to perform the evaluation.

###Running Experiments

python counterfitting.py experiment_parameters.cfg

Running the experiment loads the word vectors specified in the config file and counter-fits them to the provided linguistic constraints. The procedure prints the updated word vectors to the results directory as counter_fitted_vectors.txt (one word vector per line). The produced ranking and the gold standard ranking for the SimLex-999 pairs are also printed to the results directory.

The word_vectors directory contains the (zipped) GloVe and Paragram-300-SL999 vectors constrained to our vocabulary (these need to be unzipped before the experiments are run). The high-scoring vectors for SimLex-999 can also be found in word_vectors/counter-fitted-vectors.txt.zip (or reproduced by applying counter-fitting to Paragram vectors).

###References

The counter-fitting paper:

@InProceedings{mrksic:2016:naacl,
  author    = {Nikola Mrk\v{s}i\'c and Diarmuid {\'O S\'eaghdha} and Blaise Thomson and Milica Ga\v{s}i\'c 
  			   and Lina Rojas-Barahona and Pei-Hao Su and David Vandyke and Tsung-Hsien Wen and Steve Young},
  title     = {Counter-fitting Word Vectors to Linguistic Constraints},
  booktitle = {Proceedings of HLT-NAACL},
  year      = {2016},
}

If you are using PPDB 2.0 (Pavlick et al., 2015) or WordNet (Miller, 1995) constraints, please cite these papers. If you are using the provided pre-trained vectors, please cite (Pennington et al., 2014) for GloVe vectors and (Wieting et al., 2015) for Paragram-SL-999 vectors.