公开(公告)号：US20220114481A1

公开(公告)日：2022-04-14

申请号：US17162931

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chu Hong Hoi ,  Caiming Xiong

IPC: G06N20/00 ,  G06K9/62 ,  G06N7/00

Abstract: Embodiments described herein provide a two-stage model-agnostic approach for generating counterfactual explanation via counterfactual feature selection and counterfactual feature optimization. Given a query instance, counterfactual feature selection picks a subset of feature columns and values that can potentially change the prediction and then counterfactual feature optimization determines the best feature value for the selected feature as a counterfactual example.

公开(公告)号：US20220108086A1

公开(公告)日：2022-04-07

申请号：US17159625

申请日：2021-01-27

Applicant: salesforce.com, inc.

Inventor： Chien-Sheng Wu ,  Wenhao Liu ,  Caiming Xiong ,  Linqing Liu

IPC: G06F40/56 ,  G06F40/205

Abstract: Dialogue summarization is challenging due to its multi-speaker standpoints, casual spoken language style, and limited labelled data. The embodiments are directed to a coarse-to-fine dialogue summarization model that improves abstractive dialogue summarization quality and enables granular controllability. A summary draft that includes key words for turns in a dialogue conversation history is created. The summary draft includes pseudo-labelled interrogative pronoun categories and noisy key phrases. The dialogue conversation history is divided into segments. A generate language model is trained to generate a segment summary for each dialogue segment using a portion of the summary draft that corresponds to at least one dialogue turn in the dialogue segment. A dialogue summary is generated using the generative language model trained using the summary draft.

公开(公告)号：US11270145B2

公开(公告)日：2022-03-08

申请号：US16781179

申请日：2020-02-04

Applicant: salesforce.com, inc.

Inventor： Alexander Richard Trott ,  Caiming Xiong ,  Richard Socher

IPC: G06K9/00 ,  G06K9/46 ,  G06F16/332 ,  G06N5/04 ,  G06N3/04

Abstract: Approaches for interpretable counting for visual question answering include a digital image processor, a language processor, and a counter. The digital image processor identifies objects in an image, maps the identified objects into an embedding space, generates bounding boxes for each of the identified objects, and outputs the embedded objects paired with their bounding boxes. The language processor embeds a question into the embedding space. The scorer determines scores for the identified objects. Each respective score determines how well a corresponding one of the identified objects is responsive to the question. The counter determines a count of the objects in the digital image that are responsive to the question based on the scores. The count and a corresponding bounding box for each object included in the count are output. In some embodiments, the counter determines the count interactively based on interactions between counted and uncounted objects.

公开(公告)号：US20220058714A1

公开(公告)日：2022-02-24

申请号：US17112765

申请日：2020-12-04

Applicant: salesforce.com, inc.

Inventor： Yongjun Chen ,  Jia Li ,  Chenxi Li ,  Markus Anderle ,  Caiming Xiong ,  Simo Arajarvi ,  Harshavardhan Utharavalli

IPC: G06Q30/06 ,  G06N3/04 ,  G06N3/08

Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

公开(公告)号：US11238314B2

公开(公告)日：2022-02-01

申请号：US16686051

申请日：2019-11-15

Applicant: salesforce.com, inc.

Inventor： Ankit Chadha ,  Caiming Xiong ,  Ran Xu

IPC: G06K9/62 ,  G06T3/40 ,  G06T3/60 ,  G06N3/04 ,  G06N3/08 ,  G06T3/20

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

公开(公告)号：US20210374358A1

公开(公告)日：2021-12-02

申请号：US17010459

申请日：2020-09-02

Applicant: salesforce.com, inc.

Inventor： Congying Xia ,  Caiming Xiong

IPC: G06F40/56 ,  G06F40/284 ,  G06N7/00 ,  G06F16/9032

Abstract: Embodiments described herein provide a composed variational natural language generation (CLANG) model that is configured to generate training samples for few-shot intents. Specifically, the CLANG model may build connections between existing training samples of many-shot intents and new training samples of few-shot intents by modeling an intent as a combination of a domain and an action. In this way, the CLANG model transfers knowledge from existing many-shot intents to few-shot intents in natural language generation by learning how to compose utterances with many-shot intents and transferring such knowledge to few-shot intents.

公开(公告)号：US20210374132A1

公开(公告)日：2021-12-02

申请号：US17093885

申请日：2020-11-10

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chenxi Li ,  Latrice Barnett ,  Markus Anderle ,  Simo Arajarvi ,  Harshavardhan Utharavalli ,  Caiming Xiong ,  Richard Socher ,  Chu Hong Hoi

IPC: G06F16/2457 ,  G06N20/20

Abstract: Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.

公开(公告)号：US20210319796A1

公开(公告)日：2021-10-14

申请号：US16903964

申请日：2020-06-17

Applicant: salesforce.com, inc.

Inventor： Weiran Wang ,  Yingbo Zhou ,  Caiming Xiong

IPC: G10L15/26

Abstract: System and methods for identifying a text word from a spoken utterance are provided. An ensemble BPE system that includes a phone BPE system and a character BPE system receives a spoken utterance. Both BPE systems include a multi-level language model (LM) and an acoustic model. The phone BPE system identifies first words from the spoken utterance and determine a first score for each first word. The first words are converted into character sequences. The character BPE model converts the character sequences into second words and determines a second score for each second word. For each word from the first words that matches a word in the second words the first and second scores are combined. The text word is the word with a highest score.

公开(公告)号：US20210174798A1

公开(公告)日：2021-06-10

申请号：US16870571

申请日：2020-05-08

Applicant: salesforce.com, inc.

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

IPC: G10L15/22 ,  G06K9/62 ,  G10L15/30 ,  G10L15/16

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：US20210174028A1

公开(公告)日：2021-06-10

申请号：US17179933

申请日：2021-02-19

Applicant: salesforce.com, inc.

Inventor： Victor Zhong ,  Caiming Xiong

IPC: G06F40/30 ,  G06N3/08 ,  G06N5/02 ,  G06N3/04

Abstract: A method for maintaining a dialogue state associated with a dialogue between a user and a digital system includes receiving, by a dialogue state tracker associated with the digital system, a representation of a user communication, updating, by the dialogue state tracker, the dialogue state and providing a system response based on the updated dialogue state. The dialogue state is updated by evaluating, based on the representation of the user communication, a plurality of member scores corresponding to a plurality of ontology members of an ontology set, and selecting, based on the plurality of member scores, zero or more of the plurality of ontology members to add to or remove from the dialogue state. The dialogue state tracker includes a global-local encoder that includes a global branch and a local branch, the global branch having global trained parameters that are shared among the plurality of ontology members and the local branch having local trained parameters that are determined separately for each of the plurality of ontology members.