# VDPWI PyTorch Implementation

This is a PyTorch implementation of the following paper

* Hua He and Jimmy Lin. [Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement.](http://www.aclweb.org/anthology/N16-1108) *Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL/HLT 2016)*, pages 937-948.


Please ensure you have followed instructions in the main [README](../README.md) 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 `./get_trec_eval.sh` 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](https://dl.acm.org/citation.cfm?id=2983872).

## 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
```