公开(公告)号：US11227218B2

公开(公告)日：2022-01-18

申请号：US15980207

申请日：2018-05-15

Applicant: salesforce.com, inc.

Inventor： Sewon Min ,  Victor Zhong ,  Caiming Xiong ,  Richard Socher

IPC: G06F40/30 ,  G06N20/00 ,  G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06F40/20

Abstract: A natural language processing system that includes a sentence selector and a question answering module. The sentence selector receives a question and sentences that are associated with a context. For a question and each sentence, the sentence selector determines a score. A score represents whether the question is answerable with the sentence. Sentence selector then generates a minimum set of sentences from the scores associated with the question and sentences. The question answering module generates an answer for the question from the minimum set of sentences.

公开(公告)号：US20210397799A1

公开(公告)日：2021-12-23

申请号：US17463227

申请日：2021-08-31

Applicant: salesforce.com, inc.

Inventor： Kazuma Hashimoto ,  Raffaella Buschiazzo ,  James Bradbury ,  Teresa Anna Marshall ,  Caiming Xiong ,  Richard Socher

IPC: G06F40/58

Abstract: Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

公开(公告)号：US20210374133A1

公开(公告)日：2021-12-02

申请号：US17064466

申请日：2020-10-06

Applicant: salesforce.com, inc.

Inventor： Xi Lin ,  Caiming Xiong

IPC: G06F16/2452 ,  G06F16/21 ,  G06F16/22 ,  G06F16/242 ,  G06N3/08 ,  G06N3/04

Abstract: A text-to-database neural network architecture is provided. The architecture receives a natural language question and a database schema and generates a serialized question-schema representation that includes a question and at least one table and at least one field from the database schema. The serialized question-schema representation is appended with at least one value that matches a word in the natural language question and at least one field in a database picklist. An encoder in the architecture generates question and schema encodings from the appended question-schema representation. Schema encodings are associated with metadata that indicates a data type of the fields and whether fields are associated with primary or foreign keys. A decoder in the architecture generates an executable query from the question encodings and schema encodings.

公开(公告)号：US11113598B2

公开(公告)日：2021-09-07

申请号：US15221532

申请日：2016-07-27

Applicant: salesforce.com, inc.

Inventor： Richard Socher ,  Ankit Kumar ,  Ozan Irsoy ,  Mohit Iyyer ,  Caiming Xiong ,  Stephen Merity ,  Romain Paulus

IPC: G06N3/08 ,  G06N3/04 ,  G06F16/33 ,  G06F16/332

Abstract: A novel unified neural network framework, the dynamic memory network, is disclosed. This unified framework reduces every task in natural language processing to a question answering problem over an input sequence. Inputs and questions are used to create and connect deep memory sequences. Answers are then generated based on dynamically retrieved memories.

公开(公告)号：US11106182B2

公开(公告)日：2021-08-31

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 ,  G06N3/02 ,  G10L21/003 ,  G10L15/065 ,  G10L15/07 ,  G06K9/62

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：US20210256370A1

公开(公告)日：2021-08-19

申请号：US16950853

申请日：2020-11-17

Applicant: salesforce.com, inc.

Inventor： Govardana Sachithanandam Ramachandran ,  Ivan BRUGERE ,  Lav Varshney ,  Caiming Xiong

IPC: G06N3/08 ,  G06F9/50

Abstract: A method for using a neural network to generate an improved graph model includes receiving, by the neural network, a graph model. The graph model is based on data relating to an environment for allocating resources to a first group and a second group. The method further includes receiving, by the neural network, a budget for editing the graph model based on a cost of corresponding modification to the environment, and determining, by the neural network, a fairness representation based on a fairness requirement between the first and second groups. It is determined by the neural network, a utility function for the graph model based on first and second group utilities representing resource allocation to the first and second groups respectively. Reinforcement learning is performed on the neural network to generate the improved graph model using the utility function and the fairness representation.

公开(公告)号：US11087177B2

公开(公告)日：2021-08-10

申请号：US16176075

申请日：2018-10-31

Applicant: salesforce.com, inc.

Inventor： Lily Hu ,  Caiming Xiong ,  Richard Socher

IPC: G06K9/62 ,  G06N3/08

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.

公开(公告)号：US20210174204A1

公开(公告)日：2021-06-10

申请号：US17093478

申请日：2020-11-09

Applicant: salesforce.com, inc.

Inventor： Wenpeng Yin ,  Nazneen Rajani ,  Richard Socher ,  Caiming Xiong

IPC: G06N3/08 ,  G06F16/332 ,  G06F16/33 ,  G06F40/279 ,  G06F40/30

Abstract: A method for using a neural network model for natural language processing (NLP) includes receiving training data associated with a source domain and a target domain; and generating one or more query batches. Each query batch includes one or more source tasks associated with the source domain and one or more target tasks associated with the target domain. For each query batch, class representations are generated for each class in the source domain and the target domain. A query batch loss for the query batch is generated based on the corresponding class representations. An optimization is performed on the neural network model by adjusting its network parameters based on the query batch loss. The optimized neural network model is used to perform one or more new NLP tasks.

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.

公开(公告)号：US20210152534A1

公开(公告)日：2021-05-20

申请号：US16685806

申请日：2019-11-15

Applicant: salesforce.com, inc.

Inventor： Tian Xie ,  Caiming Xiong

IPC: H04L29/06 ,  G10L15/22 ,  G10L15/08 ,  H04L12/58 ,  H04L29/12

Abstract: A system authenticates users using voice-based conversations. The system allows the authentication process to be customized using an authentication plan. For example, the system may be a multi-tenant system that allows customization of the authentication process for each tenant. The authentication plan is represented as an expression of phrase types, each phrase type associated with a phrase verification method. The system authenticates a user by executing the expression of an authentication plan for that user in response to a request from the user. The system performs a conversation with the user according to the authentication plan. The system determines whether to allow or deny the user request based on the result of evaluation of the expression of the authentication plan.