https://github.com/smartdataanalytics/ak-de-bigru
Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge
https://github.com/smartdataanalytics/ak-de-bigru
Last synced: 5 months ago
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Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge
- Host: GitHub
- URL: https://github.com/smartdataanalytics/ak-de-bigru
- Owner: SmartDataAnalytics
- Created: 2018-08-21T18:45:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-10T10:31:01.000Z (about 2 years ago)
- Last Synced: 2024-04-16T07:41:32.153Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 42 KB
- Stars: 58
- Watchers: 17
- Forks: 13
- Open Issues: 10
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Metadata Files:
- Readme: README.md
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README
# Attention and external Knowledge augmented Dual Encoder with bi-directional GRU (AK-DE-biGRU)
Code for implementing the paper : "[Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge](https://arxiv.org/pdf/1809.03194.pdf)"
## Getting Started
We use python version 3.6.4
Install the requirements.txt file and install pytorch version: "0.3.1.post2"
### Prerequisites
Download the pre-processed files from Wu et. al, from here: https://www.dropbox.com/s/2fdn26rj6h9bpvl/ubuntu%20data.zip?dl=0and save it in ubuntu_data.
Run: python ./preprocess.py
To create the required preprocessed dataset
This will be read from data.py
Use the train.txt file to train a fasttext model using the fasttext library:https://github.com/facebookresearch/fastText by:
```
./fasttext skipgram -input train.txt -dim 200 -output fast_text_200
```
Save this file into a numpy array whose index corresponds to the word_id from the previous dictionary and the row contains the fasttext vector for that word.
copy the file to ubuntu_data directory.
Download the ubuntu_description.npy file provided and copy it to ubuntu_data directory
## Running the model
The AK-DKE-biGRU model should be run as:
```
python -u run_models.py --h_dim 300 --mb_size 32 --n_epoch 20 --gpu --lr 0.0001
```