data-analysis-vizamatrix / sentence-similarity

PyTorch implementations of various deep learning models for paraphrase detection, semantic similarity, and textual entailment

sentence-similarity

I plan to implement some models for sentence similarity found in the literature to reproduce and study them. They have a wide variety of application, including:

• Paraphrase Detection: Give two sentences, are the sentences paraphrases of each other?
• Semantic Texual Similarity: Given two sentences, how close are they in terms of semantic equivalence?
• Natural Language Inference / Textual Entailment: Can one sentence be inferred from another sentence (the premise)?
• Answer Selection: Given question-answer pairs, rank candidate answers based on relevance to question.

Setup

Install packages in requirements.txt.

Theignite library, currently in alpha, needs to be installed from source. See https://github.com/pytorch/ignite.

Download SpaCy English model:

python -m spacy download en

Compile trec_eval for computing MAP/MRR metrics for WikiQA dataset:

cd metrics
./get_trec_eval.sh

Running

Baseline

SICK

# Unsupervised
$ python main.py --model sif --dataset sick --unsupervised
Test Results - Epoch: 0 pearson: 0.7199 spearman: 0.5956
# Supervised
$ python main.py --model sif --dataset sick
Test Results - Epoch: 15 pearson: 0.7763 spearman: 0.6637
$ python main.py --model mpcnn --dataset sick
$ python main.py --model bimpm --dataset sick

WikiQA

$ python main.py --model sif --dataset wikiqa --epochs 15 --lr 0.001
Test Results - Epoch: 15 map: 0.6295 mrr: 0.6404
$ python main.py --model mpcnn --dataset wikiqa
$ python main.py --model bimpm --dataset wikiqa

Attribution

The English Wikipedia token frequency dataset for estimating p(w) in the baseline model is obtained from the official SIF implementation: https://github.com/PrincetonML/SIF.