Image Description Dataset for Language Learners
Kento Tanaka, Taichi Nishimura, Hiroaki Nanjo, Keisuke Shirai, Hirotaka Kameko, Masatake Dantsuji
Abstract
We focus on image description and a corresponding assessment system for language learners. To achieve automatic assessment of image description, we construct a novel dataset, the Language Learner Image Description (LLID) dataset, which consists of images, their descriptions, and assessment annotations. Then, we propose a novel task of automatic error correction for image description, and we develop a baseline model that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. Our experimental results show that the developed model can revise errors that cannot be revised without an image.- Anthology ID:
- 2022.lrec-1.735
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6814–6821
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.735
- DOI:
- Cite (ACL):
- Kento Tanaka, Taichi Nishimura, Hiroaki Nanjo, Keisuke Shirai, Hirotaka Kameko, and Masatake Dantsuji. 2022. Image Description Dataset for Language Learners. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6814–6821, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Image Description Dataset for Language Learners (Tanaka et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.735.pdf
- Data
- COCO