Paradox: Automatic Paraphrase Identification

Given two sentences, paradox returns a continuous valued similarity score on a scale from 0 to 5, with 0 indicating that the semantics of the sentences are completely independent and 5 signifying semantic equivalence. Paradox uses Glove pre-trained models.

How to install

Paradox is dockerized! First install Docker and then run the following commands:

cd paradox
make install
make download_glove
make download_models

Training Corpus

For training, the semantic similarity corpora from SemEval (2012-2016) are used. The training data are available under /corpus.

Evaluation

The evaluation scipt reports the results on the test data set of the SemEval2016 challange. To see the resport run the following commands:

source env/bin/activate
python benchmark.py

Citation

This repository contains the code for the DeepLDA approach introduced in the following paper. Use the following bibtex entry to cite us:

@InProceedings{liebeck-EtAl:2016:SemEval,
    author    = {Liebeck, Matthias and Pollack, Philipp and Modaresi, Pashutan and Conrad, Stefan},
    title     = {HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity},
    booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
    month     = {June},
    year      = {2016},
    address   = {San Diego, California},
    publisher = {Association for Computational Linguistics},
    pages     = {607--613},
    url       = {TOBEFILLED-http://www.aclweb.org/anthology/W/W05/W05-0292}
}

ToDos:

• Implement topical similarity based of the LDA models.