Neural Translation Reconstruction With Shared Encoder Parameters

The patent merges the encoder and reconstructor into a single integrated model structure. The encoder processes the source language input and generates hidden representations, while the reconstructor uses these same hidden representations to reconstruct the source language. By combining these functions into one model with shared parameters, the patent achieves faithful translation without proportionally increasing the total number of parameters.

The encoder in the integrated model serves multiple functions: it encodes the source language for translation and simultaneously provides hidden representations for reconstruction. This multi-functionality allows the same parameters to be utilized for both translation accuracy and faithfulness guarantee, reducing the need for separate dedicated parameters.

The patent combines the training processes of the NMT model and reconstructor into a single unified training procedure. Both the translation objective and reconstruction objective are optimized simultaneously using the same training data and computational passes, eliminating the need for separate training phases and reducing total training time.

The unified model maintains continuous learning of both translation and reconstruction capabilities throughout the training process. By optimizing both objectives simultaneously using the same training batches, the model continuously improves both functions without interruption or repetition of training passes.