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VDPWI PyTorch Implementation

This is a PyTorch implementation of the following paper

Please ensure you have followed instructions in the main README doc before running any further commands in this doc.

SICK Dataset

To run VDPWI on the SICK dataset, use the following command. If you have any problems running it check the Troubleshooting section below.

python -m vdpwi vdpwi.sick.model.castor --dataset sick --lr 5e-4 --optimizer rmsprop --epochs 15 --batch-size 8 --rnn-hidden-dim 256 --epsilon 1e-7
Implementation and config Pearson'r Spearman's p MSE
Paper 0.8784 0.8199 0.2329
PyTorch Implementation 0.8710 0.8092 0.2501

MSRVID Dataset

To run VDPWI on the MSRVID dataset, use the following command:

python -m vdpwi vdpwi.msrvid.model.castor --dataset msrvid --batch-size 16 --epochs 32 --regularization 0.0025

TrecQA Dataset

To run VDPWI on (Raw) TrecQA, you first need to run ./ in utils under the repo root while inside the utils directory. This will download and compile the official trec_eval tool used for evaluation.

Then, you can run:

python -m vdpwi vdpwi.trecqa.model --dataset trecqa --epochs 5 --regularization 0.0005 --eps 0.1 --optimizer rmsprop --lr 0.0005 --batch-size 8 --rnn-hidden-dim 256
Implementation and config MAP MRR
Paper 0.7588 0.8219
PyTorch Implementation 0.7581 0.8172

The paper results are reported in Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks.

WikiQA Dataset

You also need trec_eval for this dataset, similar to TrecQA.

Then, you can run:

python -m vdpwi vdpwi.wikiqa.model --dataset wikiqa --epochs 10 --batch-size 64 --lr 0.0005 --regularization 0.02 --optimizer rmsprop --rnn-hidden-dim 256
Implementation and config MAP MRR
Paper 0.7090 0.7234
PyTorch Implementation 0.7184 0.7286

To see all options available, use

python -m vdpwi --help
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