Cross-lingual discriminative learning of sequence models with posterior regularization
US9779087B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 13, 2013 |
| Grant date | Oct 3, 2017 |
| Priority date | — |
| Expiry date | Dec 3, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/58
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method can include obtaining (i) an aligned bi-text for a source language and a target language, and (ii) a supervised sequence model for the source language. The method can include labeling a source side of the aligned bi-text using the supervised sequence model and projecting labels from the labeled source side to a target side of the aligned bi-text to obtain a labeled target side of the aligned bi-text. The method can include filtering the labeled target side based on a task of a natural language processing (NLP) system configured to utilize a sequence model for the target language to obtain a filtered target side of the aligned bi-text. The method can also include training the sequence model for the target language using posterior regularization with soft constraints on the filtered target side to obtain a trained sequence model for the target language.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.