PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering

Zhihao Ruan, Xiaolong Hou, Lianxin Jiang


Abstract
This paper describes our system used in the SemEval-2022 Task 09: R2VQ - Competence-based Multimodal Question Answering. We propose a knowledge-enhanced model for predicting answer in QA task, this model use BERT as the backbone. We adopted two knowledge-enhanced methods in this model: the knowledge auxiliary text method and the knowledge embedding method. We also design an answer extraction task pipeline, which contains an extraction-based model, an automatic keyword labeling module, and an answer generation module. Our system ranked 3rd in task 9 and achieved an exact match score of 78.21 and a word-level F1 score of 82.62.
Anthology ID:
2022.semeval-1.179
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1274–1279
Language:
URL:
https://aclanthology.org/2022.semeval-1.179
DOI:
10.18653/v1/2022.semeval-1.179
Bibkey:
Cite (ACL):
Zhihao Ruan, Xiaolong Hou, and Lianxin Jiang. 2022. PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1274–1279, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering (Ruan et al., SemEval 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.179.pdf