公开(公告)号：US20250028751A1

公开(公告)日：2025-01-23

申请号：US18355901

申请日：2023-07-20

Applicant: Adobe Inc.

Inventor： Tong Yu ,  Kaige Xie ,  Haoliang Wang ,  Junda Wu ,  Handong Zhao ,  Ruiyi Zhang ,  Kanak Vivek Mahadik ,  Ani Nenkova

IPC: G06F16/34 ,  G06F40/35

Abstract: Dialogue skeleton assisted prompt transfer for dialogue summarization techniques are described that support training of a language model to perform dialogue summarization in a few-shot scenario. A processing device, for instance, receives a training dataset that includes training dialogues. The processing device then generates dialogue skeletons based on the training dialogues using one or more perturbation-based probes. The processing device trains a language model using prompt transfer between a source task, e.g., dialogue state tracking, and a target task, e.g., dialogue summarization, using the dialogue skeletons as supervision. The processing device then receives an input dialogue and uses the trained language model to generate a summary of the input dialogue.

公开(公告)号：US20250005289A1

公开(公告)日：2025-01-02

申请号：US18343389

申请日：2023-06-28

Applicant: Adobe Inc.

Inventor： Haoliang Wang ,  Kaige Xie ,  Tong Yu ,  Junda Wu ,  Handong Zhao ,  Ruiyi Zhang ,  Kanak Vivek Mahadik ,  Ani Nenkova

IPC: G06F40/35 ,  G06F16/34 ,  G06F16/35

Abstract: Dialogue state aware dialogue summarization techniques are described that enable generation of dialogue summaries from target domains with limited training data. A content processing system, for instance, generates one or more clusters based on training dialogues from one or more source domains. The clusters represent domain-specific features of the training dialogues and are further based on dialogue states of the training dialogues. The content processing system trains a machine learning model to generate summaries of dialogues by using the one or more clusters as prefixes in a prefix-tuning approach. The content processing system receives an input that includes a dialogue from a target domain. The content processing system generates an input prompt based on the dialogue and the one or more clusters, and the model generates a summary of the dialogue based on the input prompt.