公开(公告)号：US20230274100A1

公开(公告)日：2023-08-31

申请号：US17682282

申请日：2022-02-28

Applicant: Google LLC

Inventor： Xavier Eduardo Garcia ,  Orhan Firat ,  Noah Constant ,  Xiaoyue Guo

IPC: G06F40/58 ,  G06F40/197 ,  G06F40/166 ,  G06F40/253 ,  G06N3/08

CPC classification number: G06F40/58 ,  G06F40/197 ,  G06F40/166 ,  G06F40/253 ,  G06N3/08

Abstract: The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.

公开(公告)号：US20250148224A1

公开(公告)日：2025-05-08

申请号：US19015153

申请日：2025-01-09

Applicant: Google LLC

Inventor： Xavier Eduardo Garcia ,  Orhan Firat ,  Noah Constant ,  Xiaoyue Guo ,  Parker Riley

IPC: G06F40/58 ,  G06F40/166 ,  G06F40/197 ,  G06F40/253 ,  G06F40/56 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084

Abstract: The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.

公开(公告)号：US12210848B2

公开(公告)日：2025-01-28

申请号：US17682282

申请日：2022-02-28

Applicant: Google LLC

Inventor： Xavier Eduardo Garcia ,  Orhan Firat ,  Noah Constant ,  Xiaoyue Guo ,  Parker Riley

IPC: G06F40/58 ,  G06F40/166 ,  G06F40/197 ,  G06F40/253 ,  G06F40/56 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084

Abstract: The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.