# Genie NLP library [![Build Status](https://travis-ci.com/stanford-oval/genienlp.svg?branch=master)](https://travis-ci.com/stanford-oval/genienlp) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/stanford-oval/genienlp.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/stanford-oval/genienlp/context:python) This library contains the NLP models for the [Genie](https://github.com/stanford-oval/genie-toolkit) toolkit for virtual assistants. It is derived from the [decaNLP](https://github.com/salesforce/decaNLP) library by Salesforce, but has diverged significantly. The library is suitable for all NLP tasks that can be framed as Contextual Question Answering, that is, with 3 inputs: - text or structured input as _context_ - text input as _question_ - text or structured output as _answer_ As the [decaNLP paper](https://arxiv.org/abs/1806.08730) shows, many different NLP tasks can be framed in this way. Genie primarily uses the library for semantic parsing, dialogue state tracking, and natural language generation given a formal dialogue state, and this is what the models work best for. ## Installation genienlp is available on PyPi. You can install it with: ```bash pip3 install genienlp ``` After installation, a `genienlp` command becomes available. Likely, you will also want to download the word embeddings ahead of time: ```bash genienlp cache-embeddings --embeddings glove+char -d ``` ## Usage Train a model: ```bash genienlp train --tasks almond --train_iterations 50000 --embeddings --data --save ``` Generate predictions: ```bash genienlp predict --tasks almond --data --path ``` See `genienlp --help` for details. ## Citation If you use the MultiTask Question Answering model in your work, please cite [*The Natural Language Decathlon: Multitask Learning as Question Answering*](https://arxiv.org/abs/1806.08730). ```bibtex @article{McCann2018decaNLP, title={The Natural Language Decathlon: Multitask Learning as Question Answering}, author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher}, journal={arXiv preprint arXiv:1806.08730}, year={2018} } ``` If you use the BERT-LSTM model (Identity encoder + MQAN decoder), please cite [_Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model_](https://arxiv.org/abs/2001.05609) ```bibtex @article{Xu2020Schema2QA, title={Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model}, author={Silei Xu and Giovanni Campagna and Jian Li and Monica S. Lam}, journal={arXiv preprint arXiv:2001.05609}, year={2020} } ```