ZhengxiangShi / IngredientParsing

Dataset and pytorch codes for the paper titled "Attention-based Ingredient Phrase Parser". ESANN 2022.

IngredientParser

Here are the dataset and codes for our ESANN 2022 paper "Attention-based Ingredient Parser".

Introduction

As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation.

Applications of our ingredient parser model for conversational systems.

Assisting users to cook is one of these tasks that are expected to be solved by intelligent assistants, where ingredients and its corresponding attributes, such as name, unit, and quantity, should be provided to users precisely and promptly. To provide an engaged and successful conversational service to users for cooking tasks, we propose a new ingredient parsing model.

Model architecture.

Code

To reproduce our result, please follow the instructions below:

1. Download Pre-trained Glove Embedding

You can skip this step since data/vocab.pkl is provided.

cd data
wget https://nlp.stanford.edu/data/glove.42B.300d.zip
unzip glove.42B.300d.zip
cd ..

2. Training

python train.py --seed 0 --path saved_model_path

3. Infering

python infer.py --path saved_model_path

3. Infering

python test.py --path saved_model_path

Dataset

The source of the dataset is https://github.com/cosylabiiit/Recipedb-companion-data.

• ar_train.tsv and ar_test.tsv are from AllRecipes Food Corpus;
• gk_train.tsv and gk_test.tsv are from FOOD.com corpus;
• ar_gk_train.tsv and ar_gk_test.tsv are from AllRecipes Food Corpus and FOOD.com corpus.

Citation

@inproceedings{Shi2022attention,
title = {Attention-based Ingredient Parser},
author = {Shi, Zhengxiang and Ni, Pin and Wang, Meihui and Kim, To Eun and Lipani, Aldo},
booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
url={https://doi.org/10.14428/esann/2022.ES2022-10},
year = {2022},
address = {Bruges, Belgium},
keywords = {Conversational System, Named Entity Recognition}
}