公开(公告)号：US20230120940A1

公开(公告)日：2023-04-20

申请号：US17589693

申请日：2022-01-31

Applicant: salesforce.com, inc.

Inventor： Liang Qiu ,  Chien-Sheng Wu ,  Wenhao Liu ,  Caiming Xiong

IPC: G10L15/06 ,  G10L15/183 ,  G10L15/05

Abstract: Embodiments described herein propose an approach for unsupervised structure extraction in task-oriented dialogues. Specifically, a Slot Boundary Detection (SBD) module is adopted, for which utterances from training domains are tagged with the conventional BIO schema but without the slot names. A transformer-based classifier is trained to detect the boundary of potential slot tokens in the test domain. Next, while the state number is usually unknown, it is more reasonable to assume the slot number is given when analyzing a dialogue system. The detected tokens are clustered into the number of slot of groups. Finally, the dialogue state is represented with a vector recording the modification times of every slot. The slot values are then tracked through each dialogue session in the corpus and label utterances with their dialogue states accordingly. The semantic structure is portrayed by computing the transition frequencies among the unique states.

公开(公告)号：US11544470B2

公开(公告)日：2023-01-03

申请号：US17005316

申请日：2020-08-28

Applicant: salesforce.com, inc.

Inventor： Jianguo Zhang ,  Kazuma Hashimoto ,  Chien-Sheng Wu ,  Wenhao Liu ,  Richard Socher ,  Caiming Xiong

IPC: G06F17/00 ,  G06F40/30 ,  G10L15/16

Abstract: An online system allows user interactions using natural language expressions. The online system uses a machine learning based model to infer an intent represented by a user expression. The machine learning based model takes as input a user expression and an example expression to compute a score indicating whether the user expression matches the example expression. Based on the scores, the intent inference module determines a most applicable intent for the expression. The online system determines a confidence threshold such that user expressions indicating a high confidence are assigned the most applicable intent and user expressions indicating a low confidence are assigned an out-of-scope intent. The online system encodes the example expressions using the machine learning based model. The online system may compare an encoded user expression with encoded example expressions to identify a subset of example expressions used to determine the most applicable intent.

公开(公告)号：US11514915B2

公开(公告)日：2022-11-29

申请号：US16175639

申请日：2018-10-30

Applicant: salesforce.com, inc.

Inventor： Chien-Sheng Wu ,  Caiming Xiong ,  Richard Socher

IPC: G10L15/00 ,  G10L15/28 ,  G10L15/22 ,  G06N5/00 ,  G06F16/335 ,  G06F16/332 ,  G06F16/33

Abstract: A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global memory pointer used for filtering the knowledge base information in the memory. A decoder may generate at least one local memory pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the memory. The system generates the dialogue computer response using the local memory pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.

公开(公告)号：US20210174026A1

公开(公告)日：2021-06-10

申请号：US16870568

申请日：2020-05-08

Applicant: Salesforce.com, Inc.

Inventor： Chien-Sheng Wu ,  Chu Hong Hoi ,  Caiming Xiong

IPC: G06F40/30 ,  G06N20/00 ,  G06N5/04

Abstract: Embodiments described in this disclosure illustrate the use of self-/semi supervised approaches for label-efficient DST in task-oriented dialogue systems. Conversational behavior is modeled by next response generation and turn utterance generation tasks. Prediction consistency is strengthened by augmenting data with stochastic word dropout and label guessing. Experimental results show that by exploiting self-supervision the joint goal accuracy can be boosted with limited labeled data.