Abstract
In this study we approach the detection of null subjects and impersonal constructions in Spanish using a machine translation methodology. We repurpose the Spanish AnCora corpus, converting it to a parallel set that transforms Spanish sentences into a format that allows us to detect and classify verbs, and train LSTM-based neural machine translation systems to perform this task. Various models differing on output format and hyperparameters were evaluated. Experimental results proved this approach to be highly resource-effective, obtaining results comparable to or surpassing the state of the art found in existing literature, while employing modest computational resources. Additionally, an improved dataset for training and evaluating Spanish null-subject detection tools was elaborated for this project, that could aid in the creation and serve as a benchmark for further developments in the area.- Anthology ID:
- 2024.lrec-main.1077
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 12313–12322
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1077
- DOI:
- Cite (ACL):
- Jose Diego Suarez and Luis Chiruzzo. 2024. Null Subjects in Spanish as a Machine Translation Problem. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12313–12322, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Null Subjects in Spanish as a Machine Translation Problem (Suarez & Chiruzzo, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.1077.pdf