Abstract
In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result.- Anthology ID:
- 2020.globalex-1.15
- Volume:
- Proceedings of the 2020 Globalex Workshop on Linked Lexicography
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Ilan Kernerman, Simon Krek, John P. McCrae, Jorge Gracia, Sina Ahmadi, Besim Kabashi
- Venue:
- GLOBALEX
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 92–97
- Language:
- English
- URL:
- https://aclanthology.org/2020.globalex-1.15
- DOI:
- Cite (ACL):
- John Philip McCrae and Mihael Arcan. 2020. NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference. In Proceedings of the 2020 Globalex Workshop on Linked Lexicography, pages 92–97, Marseille, France. European Language Resources Association.
- Cite (Informal):
- NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference (McCrae & Arcan, GLOBALEX 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.globalex-1.15.pdf