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    System and methods for training task-oriented dialogue (TOD) language models
    2.
    发明授权
    System and methods for training task-oriented dialogue (TOD) language models 有权

    公开(公告)号：US11749264B2

    公开(公告)日：2023-09-05

    申请号：US17088206

    申请日：2020-11-03

    Applicant: salesforce.com, inc.

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

    IPC: G10L15/18 ,  G10L15/06

    CPC classification number: G10L15/1815 ,  G10L15/063 ,  G10L15/1822

    Abstract: Embodiments described herein provide methods and systems for training task-oriented dialogue (TOD) language models. In some embodiments, a TOD language model may receive a TOD dataset including a plurality of dialogues and a model input sequence may be generated from the dialogues using a first token prefixed to each user utterance and a second token prefixed to each system response of the dialogues. In some embodiments, the first token or the second token may be randomly replaced with a mask token to generate a masked training sequence and a masked language modeling (MLM) loss may be computed using the masked training sequence. In some embodiments, the TOD language model may be updated based on the MLM loss.

    EFFICIENT DETERMINATION OF USER INTENT FOR NATURAL LANGUAGE EXPRESSIONS BASED ON MACHINE LEARNING
    3.
    发明申请
    EFFICIENT DETERMINATION OF USER INTENT FOR NATURAL LANGUAGE EXPRESSIONS BASED ON MACHINE LEARNING 有权

    公开(公告)号：US20210374353A1

    公开(公告)日：2021-12-02

    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: 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.

    SYSTEM AND METHODS FOR TRAINING TASK-ORIENTED DIALOGUE (TOD) LANGUAGE MODELS
    7.
    发明申请
    SYSTEM AND METHODS FOR TRAINING TASK-ORIENTED DIALOGUE (TOD) LANGUAGE MODELS 有权

    公开(公告)号：US20220139384A1

    公开(公告)日：2022-05-05

    申请号：US17088206

    申请日：2020-11-03

    Applicant: salesforce.com, inc.

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

    IPC: G10L15/18 ,  G10L15/06

    Abstract: Embodiments described herein provide methods and systems for training task-oriented dialogue (TOD) language models. In some embodiments, a TOD language model may receive a TOD dataset including a plurality of dialogues and a model input sequence may be generated from the dialogues using a first token prefixed to each user utterance and a second token prefixed to each system response of the dialogues. In some embodiments, the first token or the second token may be randomly replaced with a mask token to generate a masked training sequence and a masked language modeling (MLM) loss may be computed using the masked training sequence. In some embodiments, the TOD language model may be updated based on the MLM loss.

    COARSE-TO-FINE ABSTRACTIVE DIALOGUE SUMMARIZATION WITH CONTROLLABLE GRANULARITY
    8.
    发明申请
    COARSE-TO-FINE ABSTRACTIVE DIALOGUE SUMMARIZATION WITH CONTROLLABLE GRANULARITY 有权

    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.

    Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading
    10.
    发明授权
    Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading 有权

    公开(公告)号：US11640505B2

    公开(公告)日：2023-05-02

    申请号：US16863999

    申请日：2020-04-30

    Applicant: salesforce.com, inc.

    Inventor： Yifan Gao ,  Chu Hong Hoi ,  Shafiq Rayhan Joty ,  Chien-Sheng Wu

    IPC: G06F40/289 ,  G06F16/332

    Abstract: Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.

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