Modeling ambiguity in neural machine translation
US12393795B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 28, 2022 |
| Grant date | Aug 19, 2025 |
| Priority date | — |
| Expiry date | Oct 13, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/284
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The technology addresses ambiguity in neural machine translation. An encoder module receives a given text exemplar and generates an encoded representation of it. A decoder module receives the encoded representation and a set of translation prefixes. The decoder module outputs an unbounded function corresponding to a set of tokens associated with each pair of the given text exemplar and translation prefix from the set of translation prefixes. Each token is assigned a probability between 0 and 1 in a vocabulary of the exemplar at each time step. A logits module generates, based on the unbounded function, a corresponding bounded conditional probability for each token, wherein the probabilities are not normalized over the vocabulary at each time step. A loss function module having a positive loss component and a scaled negative loss component identifies whether each target text of a set of target texts is a valid translation of the exemplar.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.