| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000373739 |
| ISSN: | 1870-9044 |
| Autores: | Mathur, Prashant1 Ruiz, Nick1 Federico, Marcello2 |
| Instituciones: | 1Universita di Trento, Trento, Trentino-Alto Adigio. Italia 2Fondazione Bruno Kessler, Trento, Trentino-Alto Adigio. Italia |
| Año: | 2012 |
| Periodo: | Jul-Dic |
| Número: | 46 |
| Paginación: | 47-53 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | In this paper we use the statistics provided by a field experiment to explore the utility of supplying machine translation suggestions in a computer-assisted translation (CAT) environment. Regression models are trained for each user in order to estimate the time to edit (TTE) for the current translation segment. We use a combination of features from the current segment and aggregated features from formerly translated segments selected with content-based filtering approaches commonly used in recommendation systems. We present and evaluate decision function heuristics to determine if machine translation output will be useful for the translator in the given segment. We find that our regression models do a reasonable job for some users in predicting TTE given only a small number of training examples; although noise in the actual TTE for seemingly similar segments yields large error margins. We propose to include the estimation of TTE in CAT recommendation systems as a well-correlated metric for translation quality |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Traducción automática, Evaluación de la calidad, Esfuerzo de traducción |
| Keyword: | Computer science, Artificial intelligence, Machine translation, Quality evaluation, Translation effort |
| Texto completo: | Texto completo (Ver HTML) |
