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公开(公告)号：US20230109681A1
公开(公告)日：2023-04-13
申请号：US17587984
申请日：2022-01-28
Applicant: salesforce.com, inc.
Inventor： Akhilesh Deepak Gotmare , Junnan Li , Chu Hong Hoi
IPC: G06F40/151 , G06F40/30 , G06F40/40 , G06N3/04
Abstract: Embodiments are directed to translating a natural language query into a code snippet in a programing language that semantically represents the query. The embodiments include a cascading neural network that includes an encoder network and a classifier network. The encoder network being faster but less accurate than the classifier network. The encoder network is trained using a contrastive learning framework to identify code candidates from a large set of code snippets. The classifier network is trained using a binary classifier to identify the code snippet that semantically represents the query from the code candidates.
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22.发明授权公开(公告)号：US11580975B2
公开(公告)日：2023-02-14
申请号：US17014458
申请日：2020-09-08
Applicant: salesforce.com, inc.
Inventor： Weishi Wang , Shafiq Rayhan Joty , Chu Hong Hoi
Abstract: Embodiments described herein provide a dynamic topic tracking mechanism that tracks how the conversation topics change from one utterance to another and use the tracking information to rank candidate responses. A pre-trained language model may be used for response selection in the multi-party conversations, which consists of two steps: (1) a topic-based pre-training to embed topic information into the language model with self-supervised learning, and (2) a multi-task learning on the pretrained model by jointly training response selection and dynamic topic prediction and disentanglement tasks.
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23.发明授权公开(公告)号：US11573957B2
公开(公告)日：2023-02-07
申请号：US16866034
申请日：2020-05-04
Applicant: salesforce.com, inc.
Inventor： Jichuan Zeng , Xi Lin , Chu Hong Hoi
IPC: G06F16/242 , G06F16/2452 , G06F40/47 , G06F40/284
Abstract: A system and method for translating questions into database queries are provided. A text to database query system receives a natural language question and a structure in a database. Question tokens are generated from the question and query tokens are generated from the structure in the database. The question tokens and query tokens are concatenated into a sentence and a sentence token is added to the sentence. A BERT network generates question hidden states for the question tokens, query hidden states for the query tokens, and a classifier hidden state for the sentence token. A translatability predictor network determines if the question is translatable or untranslatable. A decoder converts a translatable question into an executable query. A confusion span predictor network identifies a confusion span in the untranslatable question that causes the question to be untranslatable. An auto-correction module to auto-correct the tokens in the confusion span.
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公开(公告)号：US20220391755A1
公开(公告)日：2022-12-08
申请号：US17370524
申请日：2021-07-08
Applicant: salesforce.com, inc.
Inventor： Junnan Li , Chu Hong Hoi
Abstract: Embodiments described herein provide visual-and-language (V+L) systems and methods for learning vision and language representations. Specifically, a method may comprise receiving a training dataset comprising a plurality of image samples and a plurality of text samples; encoding the plurality of image samples into a plurality of encoded image samples and the plurality of text samples into a plurality of encoded text samples; computing a first loss objective based on the plurality of encoded image samples and the plurality of encoded text samples; encoding a first subset of the plurality of encoded image samples and a second subset of the plurality of encoded text samples into a plurality of encoded image-text samples; computing a second loss objective based on the plurality of encoded image-text samples; and updating the V+L model based at least in part on the first loss objective and the second loss objective.
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公开(公告)号：US20220391640A1
公开(公告)日：2022-12-08
申请号：US17532851
申请日：2021-11-22
Applicant: salesforce.com, inc.
Inventor： Chen Xing , Wenhao Liu , Chu Hong Hoi , Nitish Shirish Keskar , Caiming Xiong
Abstract: Embodiments are directed to pre-training a transformer model using more parameters for sophisticated patterns (PSP++). The transformer model is divided into a held-out model and a main model. A forward pass and a backward pass are performed on the held-out model, where the forward pass determines self-attention hidden states of the held-out model and the backward pass determines loss of the held-out model. A forward pass on the main model is performed to determine a self-attention hidden states of the main model. The self-attention hidden states of the main model are concatenated with the self-attention hidden states of the held-out model. A backward pass is performed on the main model to determine a loss of the main model. The parameters of the held-out model are updated to reflect the loss of the held-out model and parameters of the main model are updated to reflect the loss of the main model.
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Patent Agency Ranking
26.发明申请公开(公告)号：US20220358005A1
公开(公告)日：2022-11-10
申请号：US17476892
申请日：2021-09-16
Applicant: salesforce.com, inc.
Inventor： Amrita Saha , Chu Hong Hoi
IPC: G06F11/07 , G06F40/279 , G06F16/28
Abstract: Some embodiments of the current disclosure disclose methods and systems for analyzing root causes of an incident disrupting information technology services such as cloud services. In some embodiments, a set of problem review board (PRB) documents including information about said incidents may be parsed using a natural language processing (NLP) neural model to extract structured PRB data from the unstructured investigative information contained in the PRB documents. The structured PRB data may include symptoms of the incident, root causes of the incident, resolutions of the incidents, etc., and a causal knowledge graph causally relating the symptoms, root causes, resolutions of the incidents may be generated.
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公开(公告)号：US20220156593A1
公开(公告)日：2022-05-19
申请号：US17219339
申请日：2021-03-31
Applicant: salesforce.com, inc.
Inventor： Hualin Liu , Chu Hong Hoi , Junnan Li
Abstract: Embodiments described herein provide systems and methods for learning representation from unlabeled videos. Specifically, a method may comprise generating a set of strongly-augmented samples and a set of weakly-augmented samples from the unlabeled video samples; generating a set of predictive logits by inputting the set of strongly-augmented samples into a student model and a first teacher model; generating a set of artificial labels by inputting the set of weakly-augmented samples to a second teacher model that operates in parallel to the first teacher model, wherein the second teacher model shares one or more model parameters with the first teacher model; computing a loss objective based on the set of predictive logits and the set of artificial labels; updating student model parameters based on the loss objective via backpropagation; and updating the shared parameters for the first teacher model and the second teacher model based on the updated student model parameters.
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28.发明申请公开(公告)号：US20220108169A1
公开(公告)日：2022-04-07
申请号：US17162289
申请日：2021-01-29
Applicant: salesforce.com, inc.
Inventor： Amrita Saha , Shafiq Rayhan Joty , Chu Hong Hoi
Abstract: Embodiments described herein provide systems and methods for a partially supervised training model for questioning answering tasks. Specifically, the partially supervised training model may include two modules—a query parsing module and a program execution module. The query parsing module parses queries into a grogram, and the program execution module execute the program to reach an answer through explicit reasoning and partial supervision. In this way, the partially supervised training model can be trained with answers as supervision, obviating the need for supervision by gold program operations and gold query-span attention at each step of the program.
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公开(公告)号：US11288438B2
公开(公告)日：2022-03-29
申请号：US16781223
申请日：2020-02-04
Applicant: salesforce.com, inc.
Inventor： Hung Le , Chu Hong Hoi
IPC: G06K9/62 , G06F40/10 , G06N3/08 , G06F40/284 , G06K9/00
Abstract: Systems and methods are provided for performing a video-grounded dialogue task by a neural network model using bi-directional spatial-temporal reasoning. According to some embodiments, the systems and methods implement a dual network architecture or framework. This framework includes one network or reasoning module that learns dependencies between text and video in the direction of spatial→temporal, and another network or reasoning module that learns in the direction of temporal→spatial. The output of the multimodal reasoning modules may be combined to learn dependencies between language features in dialogues. The result joint representation is used as a contextual feature to the decoding components which allow the model to semantically generate meaningful responses to the users. In some embodiments, pointer networks are extended to the video-grounded dialogue task to allow the model to point to specific tokens from multiple source sequences to generate responses.
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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
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.
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