https://github.com/ppasupat/factored-span-parsing

Code for the EMNLP 2019 paper "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog"
https://github.com/ppasupat/factored-span-parsing

nlp semantic-parsing

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Code for the EMNLP 2019 paper "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog"

• Host: GitHub
• URL: https://github.com/ppasupat/factored-span-parsing
• Owner: ppasupat
• Created: 2019-08-27T16:38:37.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-05-14T18:17:47.000Z (over 3 years ago)
• Last Synced: 2023-08-20T17:24:10.214Z (about 1 year ago)
• Topics: nlp, semantic-parsing
• Language: Python
• Homepage:
• Size: 878 KB
• Stars: 6
• Watchers: 5
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog

This is the codebase for the paper

> Panupong Pasupat, Sonal Gupta, Karishma Mandyam, Rushin Shah, Mike Lewis, Luke Zettlemoyer.  

> [**Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog**](https://www.aclweb.org/anthology/D19-1163/).  

> EMNLP, 2019

## Dependencies

* Python 3.x

* PyTorch 1.x

* [PyYAML](https://pypi.org/project/PyYAML/)

* [tqdm](https://github.com/tqdm/tqdm/)

* For the GloVe + LSTM model,

  download the [processed GloVe vectors](https://nlp.stanford.edu/projects/phrasenode/glove.6B.200d.processed.zip)

  and extract the files to `data/glove/`.

* For the BERT model, [🤗Transformers](https://github.com/huggingface/transformers) is also required.

## Basic Usage

* Training with the basic model (no edge scores) on debug data:

  ```bash

  ./main.py train configs/base.yml configs/data/artificial-chain.yml \

    configs/model/embedder-lstm.yml configs/model/span-node.yml

  ```

  This will train the model on the **training** data and evaluate on the **development** data.

  The results will be saved to `out/___.exec/` where `___` is some number. The results contain

  the final config file, saved models, and predictions.

* The `out/__.exec/__.meta` metadata file stores the best accuracy and the epoch for that accuracy.

  To dump the metadata, use `./dump-meta.py out/__.exec/__.meta`.

  

* To dump the predictions of a trained model in, say, `out/42.exec/14.model` on the **test** data:

  ```bash

  ./main.py test out/42.exec/config.json -l out/42.exec/14

  ```

## Configs

* To use the real training data (TOP dataset), change the `data` config to `configs/data/top.yml`.

* To use edge scores, change the `model` config to `configs/model/span-edge.yml`.

* To use the BERT model, change both the data config and embedder config to the BERT counterparts

  (`artificial-chain.yml` > `artificial-chain-bert.yml`, `top.yml` > `top-bert.yml`,

  `embedder-lstm.yml` > `embedder-bert.yml`)

* The training accuracy will be incorrect because the decoder is not run during training.

  To turn on decoding during training, add

  ```bash

  -c "{"model": {"decoder": {"punt_on_training": false}}}"`

  ```

  to the command line.