Abstract
Aspect-Based Sentiment Analysis (ABSA)has gained much attention in recent years. It is the task of identifying fine-grained opinionpolarity towards a specific aspect associated with a given target. However, there is a lack of benchmarking platform to provide a unified environment under consistent evaluation criteria for ABSA, resulting in the difficulties for fair comparisons. In this work, we address this issue and define a benchmark, ABSA-Bench, by unifying the evaluation protocols and the pre-processed publicly available datasets in a Web-based platform. ABSA-Bench provides two means of evaluations for participants to submit their predictions or models for online evaluation. Performances are ranked in the leader board and a discussion forum is supported to serve as a collaborative platform for academics and researchers to discuss queries.- Anthology ID:
- 2020.alta-1.7
- Volume:
- Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2020
- Address:
- Virtual Workshop
- Editors:
- Maria Kim, Daniel Beck, Meladel Mistica
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 65–71
- Language:
- URL:
- https://aclanthology.org/2020.alta-1.7
- DOI:
- Cite (ACL):
- Abhishek Das and Wei Emma Zhang. 2020. ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research. In Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association, pages 65–71, Virtual Workshop. Australasian Language Technology Association.
- Cite (Informal):
- ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research (Das & Zhang, ALTA 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.alta-1.7.pdf
- Data
- LinCE, QuAC, decaNLP