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公开(公告)号：US09679558B2
公开(公告)日：2017-06-13
申请号：US14278659
申请日：2014-05-15
CPC分类号： G10L15/063 , G06F17/28 , G06F17/30654 , G06F17/30766 , G10L15/18 , G10L15/183
摘要： Systems and methods are provided for training language models using in-domain-like data collected automatically from one or more data sources. The data sources (such as text data or user-interactional data) are mined for specific types of data, including data related to style, content, and probability of relevance, which are then used for language model training. In one embodiment, a language model is trained from features extracted from a knowledge graph modified into a probabilistic graph, where entity popularities are represented and the popularity information is obtained from data sources related to the knowledge. Embodiments of language models trained from this data are particularly suitable for domain-specific conversational understanding tasks where natural language is used, such as user interaction with a game console or a personal assistant application on personal device.
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公开(公告)号：US20150370787A1
公开(公告)日：2015-12-24
申请号：US14308174
申请日：2014-06-18
发明人： Murat Akbacak , Dilek Z. Hakkani-Tur , Gokhan Tur , Larry P. Heck
IPC分类号： G06F17/28
CPC分类号： G06F17/2836 , G06F16/637 , G06F17/2818 , G10L15/06 , G10L15/183 , G10L2015/227
摘要： Systems and methods are provided for improving language models for speech recognition by adapting knowledge sources utilized by the language models to session contexts. A knowledge source, such as a knowledge graph, is used to capture and model dynamic session context based on user interaction information from usage history, such as session logs, that is mapped to the knowledge source. From sequences of user interactions, higher level intent sequences may be determined and used to form models that anticipate similar intents but with different arguments including arguments that do not necessarily appear in the usage history. In this way, the session context models may be used to determine likely next interactions or “turns” from a user, given a previous turn or turns. Language models corresponding to the likely next turns are then interpolated and provided to improve recognition accuracy of the next turn received from the user.
摘要翻译： 提供了系统和方法，用于通过将语言模型所使用的知识源适应于会话环境来改进用于语音识别的语言模型。 诸如知识图的知识源被用于基于来自映射到知识源的使用历史（例如会话日志）的用户交互信息来捕获和建模动态会话上下文。 根据用户交互序列，可以确定较高级别的意图序列，并用于形成预期相似意图但具有不同参数的模型，包括不一定出现在使用历史中的参数。 以这种方式，会话上下文模型可以用于确定来自用户的可能的下一个交互或“转弯”，给定先前的转弯或转弯。 然后内插并提供与可能的下一匝对应的语言模型，以提高从用户接收的下一匝的识别精度。
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公开(公告)号：US20150332672A1
公开(公告)日：2015-11-19
申请号：US14280070
申请日：2014-05-16
IPC分类号： G10L15/18
CPC分类号： G10L15/18 , G06F17/30654 , G06F17/30734 , G06F17/30766 , G10L15/07
摘要： Systems and methods are provided for improving language models for speech recognition by personalizing knowledge sources utilized by the language models to specific users or user-population characteristics. A knowledge source, such as a knowledge graph, is personalized for a particular user by mapping entities or user actions from usage history for the user, such as query logs, to the knowledge source. The personalized knowledge source may be used to build a personal language model by training a language model with queries corresponding to entities or entity pairs that appear in usage history. In some embodiments, a personalized knowledge source for a specific user can be extended based on personalized knowledge sources of similar users.
摘要翻译： 提供了系统和方法，用于通过将语言模型所使用的知识源个性化为特定用户或用户群体特征来改进用于语音识别的语言模型。 通过将实体或用户操作与用户的使用历史（例如查询日志）映射到知识源，为特定用户个性化知识源。 个性化知识源可以用于通过训练具有与出现在使用历史中的实体或实体对相对应的查询的语言模型来构建个人语言模型。 在一些实施例中，可以基于类似用户的个性化知识源来扩展用于特定用户的个性化知识源。
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公开(公告)号：US09997157B2
公开(公告)日：2018-06-12
申请号：US14280070
申请日：2014-05-16
CPC分类号： G10L15/18 , G06F17/30654 , G06F17/30734 , G06F17/30766 , G10L15/07
摘要： Systems and methods are provided for improving language models for speech recognition by personalizing knowledge sources utilized by the language models to specific users or user-population characteristics. A knowledge source, such as a knowledge graph, is personalized for a particular user by mapping entities or user actions from usage history for the user, such as query logs, to the knowledge source. The personalized knowledge source may be used to build a personal language model by training a language model with queries corresponding to entities or entity pairs that appear in usage history. In some embodiments, a personalized knowledge source for a specific user can be extended based on personalized knowledge sources of similar users.
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5.发明申请Eye Gaze for Spoken Language Understanding in Multi-Modal Conversational Interactions 审中-公开多语态对话中口语理解的眼睛凝视公开(公告)号：US20160091967A1
公开(公告)日：2016-03-31
申请号：US14496538
申请日：2014-09-25
发明人： Anna Prokofieva , Fethiye Asli Celikyilmaz , Dilek Z. Hakkani-Tur , Larry Heck , Malcolm Slaney
CPC分类号： G06F3/013 , G02B27/0093 , G06F3/012 , G06F3/167 , G06F2203/0381 , G06K9/00302 , G06K9/00335 , G06K9/00597 , G10L15/00 , G10L15/08 , G10L17/22
摘要： Improving accuracy in understanding and/or resolving references to visual elements in a visual context associated with a computerized conversational system is described. Techniques described herein leverage gaze input with gestures and/or speech input to improve spoken language understanding in computerized conversational systems. Leveraging gaze input and speech input improves spoken language understanding in conversational systems by improving the accuracy by which the system can resolve references—or interpret a user's intent—with respect to visual elements in a visual context. In at least one example, the techniques herein describe tracking gaze to generate gaze input, recognizing speech input, and extracting gaze features and lexical features from the user input. Based at least in part on the gaze features and lexical features, user utterances directed to visual elements in a visual context can be resolved.
摘要翻译： 描述了在与计算机对话系统相关联的视觉上下文中提高对理解和/或解析对视觉元素的引用的准确性。 本文描述的技术利用手势和/或语音输入的注视输入来改善计算机对话系统中的口语理解。 利用注视输入和语音输入通过提高系统可以在视觉上下文中解析参考或者解释用户意图的准确性来改善对话系统中的口语理解。 在至少一个示例中，本文的技术描述了跟踪注视以产生注视输入，识别语音输入，以及从用户输入提取注视特征和词汇特征。 至少部分地基于注视特征和词汇特征，可以解决针对视觉上下文中的视觉元素的用户话语。
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6.发明申请Language Modeling For Conversational Understanding Domains Using Semantic Web Resources 有权使用语义网络资源的会话理解域的语言建模公开(公告)号：US20150332670A1
公开(公告)日：2015-11-19
申请号：US14278659
申请日：2014-05-15
CPC分类号： G10L15/063 , G06F17/28 , G06F17/30654 , G06F17/30766 , G10L15/18 , G10L15/183
摘要： Systems and methods are provided for training language models using in-domain-like data collected automatically from one or more data sources. The data sources (such as text data or user-interactional data) are mined for specific types of data, including data related to style, content, and probability of relevance, which are then used for language model training. In one embodiment, a language model is trained from features extracted from a knowledge graph modified into a probabilistic graph, where entity popularities are represented and the popularity information is obtained from data sources related to the knowledge. Embodiments of language models trained from this data are particularly suitable for domain-specific conversational understanding tasks where natural language is used, such as user interaction with a game console or a personal assistant application on personal device.
摘要翻译： 提供了系统和方法，用于使用从一个或多个数据源自动收集的类似域的数据来训练语言模型。 为特定类型的数据挖掘数据源（如文本数据或用户交互数据），包括与风格，内容和相关概率相关的数据，然后将其用于语言模型培训。 在一个实施例中，从从修改为概率图的知识图中提取的特征来训练语言模型，其中表示实体流行度，并且从与知识相关的数据源获得流行度信息。 从该数据训练的语言模型的实施例特别适用于使用自然语言的领域特定对话理解任务，例如用户与个人设备上的游戏控制台或个人助理应用程序的交互。
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公开(公告)号：US20150248886A1
公开(公告)日：2015-09-03
申请号：US14194964
申请日：2014-03-03
发明人： Ruhi Sarikaya , Fethiye Asli Celikyilmaz , Zhaleh Feizollahi , Larry Paul Heck , Dilek Z. Hakkani-Tur
IPC分类号： G10L15/18
CPC分类号： G10L15/1815 , G06F3/167 , G06F17/30654 , G10L15/00 , G10L15/22 , G10L2015/223 , G10L2015/228
摘要： A model-based approach for on-screen item selection and disambiguation is provided. An utterance may be received by a computing device in response to a display of a list of items for selection on a display screen. A disambiguation model may then be applied to the utterance. The disambiguation model may be utilized to determine whether the utterance is directed to at least one of the list of displayed items, extract referential features from the utterance and identify an item from the list corresponding to the utterance, based on the extracted referential features. The computing device may then perform an action which includes selecting the identified item associated with utterance.
摘要翻译： 提供了一种基于模型的屏幕选项和消歧歧义方法。 响应于在显示屏幕上显示用于选择的项目的列表，计算设备可以接收发声。 然后可以将消歧模型应用于话语。 消歧模型可以用于确定话语是否被引导到所显示的项目的列表中的至少一个，基于所提取的参考特征，从话语中提取参考特征并从对应于话语的列表中识别项目。 然后，计算设备可以执行包括选择与话语相关联的所识别的项目的动作。
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8.发明申请DEEP LEARNING FOR SEMANTIC PARSING INCLUDING SEMANTIC UTTERANCE CLASSIFICATION 审中-公开深度学习用于语义分段，包括语义分类公开(公告)号：US20150310862A1
公开(公告)日：2015-10-29
申请号：US14260419
申请日：2014-04-24
CPC分类号： G10L15/1815 , G06F17/2785 , G06N3/08 , G10L15/16 , G10L15/26
摘要： One or more aspects of the subject disclosure are directed towards performing a semantic parsing task, such as classifying text corresponding to a spoken utterance into a class. Feature data representative of input data is provided to a semantic parsing mechanism that uses a deep model trained at least in part via unsupervised learning using unlabeled data. For example, if used in a classification task, a classifier may use an associated deep neural network that is trained to have an embeddings layer corresponding to at least one of words, phrases, or sentences. The layers are learned from unlabeled data, such as query click log data.
摘要翻译： 主题公开的一个或多个方面涉及执行语义解析任务，例如将与口语发音相对应的文本分类到类中。 表示输入数据的特征数据被提供给语义解析机制，其使用至少部分地通过使用未标记数据的无监督学习训练的深层模型。 例如，如果在分类任务中使用，分类器可以使用被训练为具有对应于词，短语或句子中的至少一个的嵌入层的相关联的深层神经网络。 这些层是从未标记的数据中学习的，例如查询点击日志数据。
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公开(公告)号：US20150178273A1
公开(公告)日：2015-06-25
申请号：US14136919
申请日：2013-12-20
IPC分类号： G06F17/28
CPC分类号： G06F17/28
摘要： A relation detection model training solution. The relation detection model training solution mines freely available resources from the World Wide Web to train a relationship detection model for use during linguistic processing. The relation detection model training system searches the web for pairs of entities extracted from a knowledge graph that are connected by a specific relation. Performance is enhanced by clipping search snippets to extract patterns that connect the two entities in a dependency tree and refining the annotations of the relations according to other related entities in the knowledge graph. The relation detection model training solution scales to other domains and languages, pushing the burden from natural language semantic parsing to knowledge base population. The relation detection model training solution exhibits performance comparable to supervised solutions, which require design, collection, and manual labeling of natural language data.
摘要翻译： 关系检测模型训练解决方案。 关系检测模型训练解决方案从万维网上免费提供资源，培养语言处理中使用的关系检测模型。 关系检测模型训练系统在网络上搜索通过特定关系连接的知识图提取的实体对。 通过剪切搜索片段来提取性能，以提取连接依赖关系树中的两个实体的模式，并根据知识图中的其他相关实体细化关系的注释。 关系检测模型训练解决方案扩展到其他领域和语言，将自然语言语义解析的负担推向知识库人口。 关系检测模型训练解决方案表现出与监督解决方案相当的性能，需要对自然语言数据进行设计，收集和手动标注。
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