Abstract
The objective of interactive translation prediction (ITP), a paradigm of computer-aided translation, is to assist professional translators by offering context-based computer-generated suggestions as they type. While most state-of-the-art ITP systems are tightly coupled to a machine translation (MT) system (often created ad-hoc for this purpose), our proposal follows a resourceagnostic approach, one that does not need access to the inner workings of the bilingual resources (MT systems or any other bilingual resources) used to generate the suggestions, thus allowing to include new resources almost seamlessly. As we do not expect the user to tolerate more than a few proposals each time, the set of potential suggestions need to be filtered and ranked; the resource-agnostic approach has been evaluated before using a set of intuitive length-based and position-based heuristics designed to determine which suggestions to show, achieving promising results. In this paper, we propose a more principled suggestion ranking approach using a regressor (a multilayer perceptron) that achieves significantly better results.- Anthology ID:
- 2016.amta-researchers.6
- Volume:
- Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track
- Month:
- October 28 - November 1
- Year:
- 2016
- Address:
- Austin, TX, USA
- Editors:
- Spence Green, Lane Schwartz
- Venue:
- AMTA
- SIG:
- Publisher:
- The Association for Machine Translation in the Americas
- Note:
- Pages:
- 65–78
- Language:
- URL:
- https://aclanthology.org/2016.amta-researchers.6
- DOI:
- Cite (ACL):
- Daniel Torregrosa, Juan Antonio Pérez-Ortiz, and Mikel Forcada. 2016. Ranking suggestions for black-box interactive translation prediction systems with multilayer perceptrons. In Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track, pages 65–78, Austin, TX, USA. The Association for Machine Translation in the Americas.
- Cite (Informal):
- Ranking suggestions for black-box interactive translation prediction systems with multilayer perceptrons (Torregrosa et al., AMTA 2016)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2016.amta-researchers.6.pdf