Code for Mimicking Word Embeddings using Subword RNNs (EMNLP 2017)

I'm adding details to this documentation as I go. When I'm through, this comment will be gone.

The main dependency for this project is DyNet. Get it here. Their 2.0 version has just been released, and I hope to upgrade this project and models to that version at some point.

The mimick directory contains scripts relevant to the Mimick model: dataset creation, model creation, intrinsic analysis. The models directory within contains models trained for all 23 languages mentioned in the paper. If you're using the pre-trained models, you don't need anything else from the mimick directory in order to run the tagging model. If you train new models, please add them here via pull request!

The root directory of this repository contains the code required to perform extrinsic analysis on Universal Dependencies data. Vocabulary files are supplied in the vocabs directory.

The entry point is model.py, which can use tagging datasets created using the make_dataset.py script. Note that model.py accepts pre-trained Word Embedding models via text files with no header. For Mimick models, this exact format is output into the path in mimick/model.py script's --output argument. For Word2Vec, FastText, or Polyglot models, one can create such a file using the scripts/output_word_vectors.py script that accepts a model (.pkl or .bin) and the desired output vocabulary (.txt).