Abstract
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly. In this work we propose a simple personalised method for this task, in which speakers are grouped into profiles according to their referential behaviour. Intrinsic evaluation shows that the use of speaker’s profiles generally outperforms the personalised method found in previous work.- Anthology ID:
- W17-3536
- Volume:
- Proceedings of the 10th International Conference on Natural Language Generation
- Month:
- September
- Year:
- 2017
- Address:
- Santiago de Compostela, Spain
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 233–237
- Language:
- URL:
- https://aclanthology.org/W17-3536
- DOI:
- 10.18653/v1/W17-3536
- Cite (ACL):
- Thiago Castro Ferreira and Ivandré Paraboni. 2017. Improving the generation of personalised descriptions. In Proceedings of the 10th International Conference on Natural Language Generation, pages 233–237, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Improving the generation of personalised descriptions (Castro Ferreira & Paraboni, INLG 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-3536.pdf