Shi, Zhengxiang;
Ni, Pin;
Kim, To Eun;
Wang, Meihui;
Lipani, Aldo;
(2022)
Attention-based Ingredient Phrase Parser.
In:
Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022).
(pp. pp. 411-416).
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022): Bruges, Belgium (Virtual).
Preview |
Text
Attention-based Ingredient Phrase Parser CameraReadyVersion.pdf - Published Version Download (443kB) | Preview |
Abstract
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. Assisting users to cook is one of these tasks that are expected to be solved by intelligent assistants, where ingredients and their corresponding attributes, such as name, unit, and quantity, should be provided to users precisely and promptly. However, existing ingredient information scraped from the cooking website is in the unstructured form with huge variation in the lexical structure, for example, “1 garlic clove, crushed”, and “1 (8 ounce) package cream cheese, softened”, making it difficult to extract information exactly. To provide an engaged and successful conversational service to users for cooking tasks, we propose a new ingredient parsing model that can parse an ingredient phrase of recipes into the structure form with its corresponding attributes with over 0.93 F1-score. Experimental results show that our model achieves state-of-the-art performance on AllRecipes and Food.com datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | Attention-based Ingredient Phrase Parser |
| Event: | 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022) |
| Location: | Bruges, Belgium |
| Dates: | 5 Oct 2022 - 7 Oct 2022 |
| ISBN-13: | 978287587084-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.14428/esann/2022.es2022-10 |
| Publisher version: | https://doi.org/10.14428/esann/2022.es2022-10 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150873 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
