Abstract
In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.- Anthology ID:
- 2021.findings-emnlp.333
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3957–3972
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.333
- DOI:
- 10.18653/v1/2021.findings-emnlp.333
- Cite (ACL):
- Iker García-Ferrero, Rodrigo Agerri, and German Rigau. 2021. Benchmarking Meta-embeddings: What Works and What Does Not. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3957–3972, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Benchmarking Meta-embeddings: What Works and What Does Not (García-Ferrero et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-emnlp.333.pdf
- Code
- ikergarcia1996/metavec
- Data
- CoLA, ConceptNet, GLUE, MRPC, MultiNLI, QNLI, SST, SST-2