An Attention-based Sequence-to-Sequence Model for Slot Filling
A sequence-to-sequence model with attention and beam search for slot filling. Implemented in PyTorch and trained and evaluated on the ATIS dataset. Check the report for more implementation details.
Get started
- Clone the repository
- Install the required dependencies by running
pip install -r requirements.txt - From the repository root, open and run the Jupyter notebook
Seq2Seq.ipynb
Note: the model was developed and tested using Python 3.7.10.
Project structure
All the code to define the model and run the experiments is available in Seq2Seq.ipynb.
The folder data contains the ATIS dataset, the conlleval.pl script and the pre-trained embedding weights, obtained using the gensim APIs and filtered to contain only the relevant tokens.
The project report is available in report.pdf.