公开(公告)号：US12136413B1

公开(公告)日：2024-11-05

申请号：US17710762

申请日：2022-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Saket Dingliwal ,  Sravan Babu Bodapati ,  Katrin Kirchhoff ,  Ankur Gandhe ,  Anubhav Mishra ,  John Baker ,  Ashish Vishwanath Shenoy ,  Ravi Teja Gadde

IPC: G06F40/40 ,  G10L15/06 ,  G10L15/183

Abstract: Domain-specific parameters may be used for tuning speech processing. A pre-trained transformer-based language model may train domain-specific parameters using domain-specific unlabeled text data. This domain-specific parameters can then be appended to candidate texts produced by a speech model on received speech data and input to the transformer-based language model to score the candidate texts. The scores of the candidate texts determined using the pre-trained transformer-based language model can then be used to select a candidate text for further speech processing.

公开(公告)号：US20250005298A1

公开(公告)日：2025-01-02

申请号：US18344742

申请日：2023-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Saket Dingliwal ,  Karthik Gopalakrishnan ,  Sravan Babu Bodapati ,  Sarthak Handa ,  Katrin Kirchhoff

IPC: G06F40/40 ,  G06F40/20

Abstract: Pairs of text collections are obtained. An individual pair comprises (a) a source text collection which includes a first group of text sequences and (b) an annotated analysis result of the source text collection, comprising a second group of text sequences and a set of evidence mappings generated by an evidence mapping model. An evidence mapping indicates, for a particular text sequence of the second group, another text sequence of the first group which provides evidence for the particular text sequence. A quality metric of the model is obtained using an automated evaluation methodology in which a question is generated from the particular text sequence, and an analysis of a pair of answers (including an answer generated using an evidence mapping) to the question is performed. The quality metric is provided via a programmatic interface.

公开(公告)号：US20250005282A1

公开(公告)日：2025-01-02

申请号：US18344764

申请日：2023-06-29

Applicant: Amazon Technologies, Inc.

Inventor： John Colton Moriarty ,  Saket Dingliwal ,  Karthik Gopalakrishnan ,  Sravan Babu Bodapati ,  Katrin Kirchhoff ,  Lei Xu

IPC: G06F40/284 ,  G06F16/34

Abstract: Domain specialty instructions may be generated for performing text analysis tasks. An input text may be received for performing a text analysis task. One or more domain entities may be extracted from the input text using a machine learning model trained to recognize entities of a domain in a given text. The one or more domain entities may be inserted as part of generating instructions to perform the text analysis task using a pre-trained machine learning model fine-tuned to the domain. The pre-trained machine learning model may be caused to perform the text analysis task using the generated instructions and a result of the text analysis task may be provided.

公开(公告)号：US20250005063A1

公开(公告)日：2025-01-02

申请号：US18344739

申请日：2023-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Devang Kulshreshtha ,  Saket Dingliwal ,  Sravan Babu Bodapati ,  Katrin Kirchhoff ,  Sarthak Handa

IPC: G06F16/34 ,  G06F40/169 ,  G06F40/40

Abstract: Pairs of text collections are obtained. An individual pair comprises (a) a source text collection which includes a first group of text sequences and (b) an annotated analysis result of the source text collection, comprising a second group of text sequences and a set of evidence mappings generated by an evidence mapping model. An evidence mapping indicates, for a particular text sequence of the second group, another text sequence of the first group which provides evidence for the particular text sequence. A quality metric of the model is obtained using an automated evaluation methodology in which a question is generated from the particular text sequence, and an analysis of a pair of answers (including 10 an answer generated using an evidence mapping) to the question is performed. The quality metric is provided via a programmatic interface.