公开(公告)号：US20220103491A1

公开(公告)日：2022-03-31

申请号：US17037554

申请日：2020-09-29

Applicant: salesforce.com, inc.

Inventor： Xinyi Yang ,  Tian Xie ,  Caiming Xiong ,  Wenhao Liu ,  Huan Wang ,  Kazuma Hashimoto ,  Jin Qu ,  Feihong Wu ,  Yingbo Zhou

IPC: H04L12/58 ,  G06N3/04 ,  G06K9/62 ,  G06F40/35

Abstract: A conversation engine performs conversations with users using chatbots customized for performing a set of tasks that can be performed using an online system. The conversation engine loads a chatbot configuration that specifies the behavior of a chatbot including the tasks that can be performed by the chatbot, the types of entities relevant to each task, and so on. The conversation may be voice based and use natural language. The conversation engine may load different chatbot configurations to implement different chatbots. The conversation engine receives a conversation engine configuration that specifies the behavior of the conversation engine across chatbots. The system may be a multi-tenant system that allows customization of the chatbots for each tenant.

公开(公告)号：US20210389736A1

公开(公告)日：2021-12-16

申请号：US17460691

申请日：2021-08-30

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G05B13/02 ,  G10L21/003 ,  G06N3/02

Abstract: A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.

公开(公告)号：US20210343274A1

公开(公告)日：2021-11-04

申请号：US16993797

申请日：2020-08-14

Applicant: salesforce.com, inc.

Inventor： Young Mo Kang ,  Yingbo Zhou

IPC: G10L15/06 ,  G10L15/26 ,  G10L15/16

Abstract: An automatic speech recognition (ASR) system that determines a textual representation of a word from a word spoken in a natural language is provided. The ASR system uses an acoustic model, a language model, and a decoder. When the ASR system receives a spoken word, the acoustic model generates word candidates for the spoken word. The language model determines an n-gram score for each word candidate. The n-gram score includes a base score and a bias score. The bias score is based on a logarithmic probability of the word candidate, where the logarithmic probability is derived using a class-based language model where the words are clustered into non-overlapping clusters according to word statistics. The decoder decodes a textual representation of the spoken word from the word candidates and the corresponding n-gram score for each word candidate.

公开(公告)号：US11056099B2

公开(公告)日：2021-07-06

申请号：US16562257

申请日：2019-09-05

Applicant: salesforce.com, inc.

Inventor： Yingbo Zhou ,  Caiming Xiong

IPC: G10L15/06 ,  G06N3/08 ,  G10L15/14 ,  G10L15/16 ,  G06N3/04 ,  G06N7/00 ,  G10L25/51

Abstract: The disclosed technology teaches a deep end-to-end speech recognition model, including using multi-objective learning criteria to train a deep end-to-end speech recognition model on training data comprising speech samples temporally labeled with ground truth transcriptions. The multi-objective learning criteria updates model parameters of the model over one thousand to millions of backpropagation iterations by combining, at each iteration, a maximum likelihood objective function that modifies the model parameters to maximize a probability of outputting a correct transcription and a policy gradient function that modifies the model parameters to maximize a positive reward defined based on a non-differentiable performance metric which penalizes incorrect transcriptions in accordance with their conformity to corresponding ground truth transcriptions; and upon convergence after a final backpropagation iteration, persisting the modified model parameters learned by using the multi-objective learning criteria with the model to be applied to further end-to-end speech recognition.

公开(公告)号：US20190295530A1

公开(公告)日：2019-09-26

申请号：US16027111

申请日：2018-07-03

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G10L15/07 ,  G06N3/08 ,  G10L15/16 ,  G10L15/22

Abstract: A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.

公开(公告)号：US20190286073A1

公开(公告)日：2019-09-19

申请号：US16054935

申请日：2018-08-03

Applicant: salesforce.com, inc.

Inventor： Ehsan Hosseini-Asl ,  Caiming Xiong ,  Yingbo Zhou ,  Richard Socher

IPC: G05B13/02

Abstract: A method for training parameters of a first domain adaptation model includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain. The evaluating the cycle consistency objective is based on one or more first training representations adapted from the first domain to the second domain by a first domain adaptation model and from the second domain to the first domain by a second domain adaptation model, and one or more second training representations adapted from the second domain to the first domain by the second domain adaptation model and from the first domain to the second domain by the first domain adaptation model. The method further includes evaluating a learning objective based on the cycle consistency objective, and updating parameters of the first domain adaptation model based on learning objective.

公开(公告)号：US20190149834A1

公开(公告)日：2019-05-16

申请号：US15874515

申请日：2018-01-18

Applicant: salesforce.com, inc.

Inventor： Yingbo Zhou ,  Luowei ZHOU ,  Caiming XIONG ,  Richard SOCHER

IPC: H04N19/46 ,  H04N19/44 ,  H04N19/60 ,  H04N19/187 ,  H04N19/132 ,  H04N19/33 ,  H04N19/126 ,  H04N21/488 ,  H04N21/81

CPC classification number: H04N19/46 ,  H04N19/126 ,  H04N19/132 ,  H04N19/187 ,  H04N19/33 ,  H04N19/44 ,  H04N19/60 ,  H04N21/4884 ,  H04N21/8126

Abstract: Systems and methods for dense captioning of a video include a multi-layer encoder stack configured to receive information extracted from a plurality of video frames, a proposal decoder coupled to the encoder stack and configured to receive one or more outputs from the encoder stack, a masking unit configured to mask the one or more outputs from the encoder stack according to one or more outputs from the proposal decoder, and a decoder stack coupled to the masking unit and configured to receive the masked one or more outputs from the encoder stack. Generating the dense captioning based on one or more outputs of the decoder stack. In some embodiments, the one or more outputs from the proposal decoder include a differentiable mask. In some embodiments, during training, error in the dense captioning is back propagated to the decoder stack, the encoder stack, and the proposal decoder.